E2 · Publication Volume 24

Core Exploration Entities

project, tenure, site, hole, survey, sample and assay

Learning objectives

• Explain why project, tenure, site, hole, survey, sample and assay require explicit semantic modelling.
• Design identities, relations and constraints that preserve core exploration entities across exchange.
• Separate hard release gates from diagnostic metrics and interpretation choices.
• Produce a constrained exploration entity graph with lifecycle and relationship tests from synthetic evidence.

The lesson is complete only when the learner can defend both the model and the release decision. A neat schema without evidence, tests or declared limitations is an unverified design. The assessed artefact must make assumptions visible and distinguish source assertions from derived conclusions.

Decision context

The model must support questions such as which samples came from a hole, which survey governed a downhole position, and which result was reported for a sample. These joins should follow explicit identifiers and cardinalities. A folder hierarchy or repeated text label is not a dependable relationship.

Start with a decision record: name the intended use, the evidence required, the consequence of error, the accepted uncertainty and the role authorised to accept residual risk. Then ask whether the proposed model can answer the decision question without relying on filename conventions, row order, undocumented defaults or someone’s memory. This prevents technology selection from concealing a missing semantic requirement.

The same record may be fit for one use and unfit for another. A rapid exploratory view can tolerate conditions that a released exchange package cannot. Fitness is therefore stated against a use, contract version and quality gate rather than attached permanently to the data.

Core concept

Exploration data becomes auditable when records refer to explicit entities with stable identities and declared relationships. A project groups work but does not replace a tenure, site, hole, survey station, sample or assay result. Each entity has its own lifecycle, evidence and validity conditions.

The working scope is project, tenure, site, hole, survey, sample and assay. For each item in that scope, distinguish the thing itself, the label used by a source, the claim made about it and the record that carries the claim. Identity is not a display name; a value is not its unit; an observation is not a model; current is not the same as valid. These distinctions create explicit places for correction, uncertainty and competing interpretations.

A good semantic design can be explained as a set of sentences before it is encoded. Each sentence identifies a subject, a property or relationship, an object or result, and the context under which the claim holds. Physical tables and files are then projections of those sentences, not their source of meaning.

Semantic model

Use a small entity graph. A project relates to zero or more tenure records and sites; a site may host one or more holes; a hole has collar and survey observations; a sample has a support interval or point and may produce several analytical results. Method, laboratory batch and quality-control role belong to the result process, not to the physical sample identity.

Test every proposed record against seven questions: What has identity? What type is it? Which property or relationship is asserted? Which spatial and temporal context applies? Which state or qualifier modifies the assertion? Which evidence supports it? Which version and activity produced the stored representation? Missing answers become explicit contract gaps.

Normalisation is used to separate independent facts, not to maximise the number of tables. A compact nested object can be semantically sound if the same identities, constraints and provenance remain explicit. Conversely, a highly normalised database can still be ambiguous when relationships and units exist only in documentation or application code.

Constraints and invariants

| Invariant | Executable or review test | | --- | --- | | One entity, one durable identity | Do not reuse an identifier after cancellation or physical replacement. | | Cardinality is declared | Validate required parent links and permitted one-to-many relationships. | | Process records are not objects | Keep sampling and assaying events separate from sample identity. | | Spatial support is explicit | A sample declares point, interval, volume or composite support. |

An invariant is a condition that must remain true across storage, export, correction and reprocessing. Implement it as close to the authoritative boundary as practical and repeat the check at exchange boundaries. Record rule identifier, version, severity, evaluated scope, observed value and outcome so a failure can be reproduced.

Hard gates protect identity, semantic validity, required provenance and authorised use. Diagnostic checks reveal unusual values or patterns but require interpretation. Never convert a diagnostic threshold into deletion or correction without a reviewed rule and preserved source evidence.

Quantitative reasoning

For each relationship type, compute orphan rate R_o = n_o / n_f, where n_o is the number of foreign identifiers without a valid target and n_f is the number of non-empty foreign identifiers. Also test cardinality violations separately; a zero orphan rate does not reveal two active collar records where only one is permitted.

Every reported ratio states its numerator, denominator, exclusions and evaluation time. Stratify results by source, entity type, contract version or processing run where aggregation could hide a local failure. Counts accompany percentages so a seemingly large change based on a tiny denominator remains visible.

Precision is part of meaning. Do not add decimal places merely because a storage type permits them, and do not round identity, interval or coordinate fields without a declared tolerance and test. Quantitative summaries support a release decision; they do not replace semantic review.

Evidence and uncertainty

Entity existence and relationship validity require evidence. A hole identifier may be asserted before drilling, while its completed depth remains unknown. Preserve planned, commenced, completed, abandoned and superseded states rather than inferring existence from whichever table happens to contain a row.

Build an evidence packet containing preserved source reference, acquisition or assertion context, applicable method, validation results, reviewer decision and links to derivatives. Classify uncertainty as observational, semantic, structural, parametric or policy-related where that distinction changes treatment. “Unknown” is a valid state when the evidence cannot justify a stronger claim.

Contradictory evidence remains available. The model may select one current assertion, but the reason, competing assertion and effective time are retained. This makes later reinterpretation possible without pretending the earlier evidence never existed.

Interfaces and storage

Exchange a compact entity registry before large observation tables. The registry provides identifiers, entity type, status, parent links, validity dates and aliases. Consumers can then validate references early and report a precise missing entity rather than a vague import failure.

Design an interface from the logical contract outward. Specify identifiers, types, cardinalities, units, value states, coordinate and time references, version negotiation, validation behaviour and structured errors before choosing a serialisation. The physical representation then declares its mapping to those logical elements.

Storage optimisation may partition, compress, index or cache data, but it must not change identity or silently remove context. A derived representation points to immutable inputs and a processing manifest. A cache carries freshness and contract-version information and is never treated as the only evidence copy.

Governance and access

Define which role may create, merge, retire or correct each entity type. High-impact entities such as holes and samples need controlled correction because identity changes propagate into intervals, assays, models and reports. Corrections retain the prior assertion and reason.

Governance is expressed through named roles, review states and versioned decisions, not through references to a particular organisation. Define who may propose, validate, approve, supersede and withdraw each governed resource. The audit trail records the role and event while avoiding unnecessary personal data.

Apply least-necessary access to source evidence and derivatives. Access controls must not erase identifiers, lineage or quality metadata needed to understand an authorised release. When policy is unresolved, quarantine the output with a precise reason and escalation route.

Integration checkpoint

The diagram summarises the control flow for this lesson. Read it from source evidence through semantic structure and validation to a decision-ready artefact. Each arrow should correspond to a declared relationship or transformation; each boundary should have a contract; each released node should have an identity, version and provenance pointer.

Integrate the lesson by adding a constrained exploration entity graph with lifecycle and relationship tests to the evolving synthetic data package. Verify that earlier artefacts still resolve and that the new model does not overwrite observations, identifiers, values or versions introduced in previous lessons. Record every changed assumption.

Synthetic worked example

A synthetic dataset contains two text spellings for one hole and a sample table that refers to both. The learner creates one durable hole identity, records the two labels as aliases, checks that collar and survey records refer to that identity, and refuses to merge a third similarly named hole because its site and collar evidence differ.

Work the example in four passes:

1. Preserve the received records and write the intended decision without correcting anything.
2. Identify entities, claims, context, uncertainties and policy constraints; mark every unresolved item.
3. Apply the versioned rules, create derivatives and record the exact transformation plus validation evidence.
4. Issue an accept, reject or quarantine decision and show how an independent reviewer can reproduce it.

Because the example is entirely synthetic, its values demonstrate method only. The important result is the chain from received evidence to justified decision. If a required fact is absent, the worked solution records the gap rather than manufacturing a plausible value.

Practice task

Draw the project–site–hole–survey–sample–result graph for a synthetic campaign. Label each edge with optionality and cardinality, add lifecycle states, then write validation queries in plain language for missing parents, duplicate active children and impossible support relationships.

Use the following acceptance criteria:

• All required identifiers and references resolve to declared types.
• Every transformation preserves the received evidence and records its derivation.
• Invalid, unknown and inapplicable states remain distinct and machine-testable.
• The output identifies the contract, vocabulary and processing versions used.
• A second reader can reproduce the validation result without private knowledge.

Submit the source snapshot, authored contract or model, validation output, derivative, manifest and a short decision record. A screenshot alone is insufficient because it cannot demonstrate the exact input, version or rule execution.

Common failure modes

• Project names are used as if they were immutable identifiers.
• A result row is treated as the physical sample.
• Hole labels are merged using spelling alone.
• A sample has no declared point or interval support.

These failures share a pattern: convenient representation is mistaken for verified meaning. Diagnose the earliest boundary at which an assumption became implicit. Correct by restoring source evidence, making the assumption a versioned field or rule, rerunning dependent transformations and superseding—not overwriting—the affected release.

Do not repair a failure by adding an undocumented default. A blocked result with a specific missing dependency is safer and more reusable than a complete-looking result whose meaning cannot be reconstructed.

Review questions

1. Why is a project not a substitute for a site or tenure?
2. Where should analytical method identity reside?
3. What evidence justifies merging two hole labels?
4. Which relationship checks expose impossible samples?

For each answer, identify the governing invariant, the evidence needed to evaluate it and the appropriate release behaviour when the invariant fails. A strong answer distinguishes scientific uncertainty from missing semantics and distinguishes a recoverable warning from a hard contract violation.

Sources and further reading

• OGC GeoSciML 4.1, an exchange model for geologic features, observations and vocabularies.
• W3C PROV-O, a formal vocabulary for entities, activities, agents and derivation.
• ISO 19115-1 geographic metadata, a conceptual structure for describing geographic resources.
• RFC 9562, current UUID formats, generation rules and representation.