Interpretation and Representation in Geomodels: The POKIMON Ontology for Formalizing Geomodelling Knowledge
Bibliographic record
Abstract
Abstract. With their growing volumes and uses, it is increasingly important to understand the interpretative and representational aspects of three-dimensional (3D) geosciences models. Such understanding will not only clarify key premises, inferences, and conclusions, but also enable more informed applications. Yet the epistemic foundations are often opaque. Critical information about assumptions, reasoning steps, and uncertainties typically remains tacit in the mind of the geomodeller. This lack of transparency hampers explainability, reproducibility, and broader utility. Current practices therefore 15 limit trust, knowledge transfer, and automation in geomodelling workflows. To address these limitations, we develop the POKIMON ontology, designed to make explicit the expert knowledge, interpretative choices, and conceptual structures underlying 3D geosciences models. POKIMON provides a formalized framework to represent how geological and geomdelling concepts are applied during model construction. Motivating use-cases, the ontological structure, and its application to the use[1]cases are presented to demonstrate utility and to advance automated knowledge-driven 3D geomodelling.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".