Metasomatic Mineral Systems with IOA, IOCG, and Affiliated Deposits: Ontology, Taxonomy, Lexicons, and Field Geology Data Collection Strategy
Bibliographic record
Abstract
Metasomatic iron and alkali-calcic (MIAC) mineral systems form district-scale metasomatic footprints in the upper crust that are genetically associated with iron oxide–apatite (IOA), iron oxide and iron sulfide copper–gold (IOCG, ISCG), skarn, and affiliated critical and precious metal deposits. The development of MIAC systems is characterized by series of alteration facies that form key mappable entities in the field and along drill cores. Each facies can precipitate deposit types specific to the facies or host deposits formed at a subsequent facies. Defining the spatial and temporal relations between alteration facies and host rocks as well as with pre, syn, and post MIAC magmatic, tectonic, and mineralization events is essential to understanding the evolution of a MIAC system and to evaluating its overall mineral prospectivity. This paper proposes an ontology for MIAC systems that frames the key characteristics of the main alteration facies described and links it to a taxonomy and descriptive lexicons that allow the user to build an efficient data collection system tailored to the description of MIAC systems. The application developed by the Geological Survey of Canada for collecting field data is used as an example. The data collection system, including the application for collecting field data and the lexicons, are applicable to regional- and deposit-scale geological mapping as well as to drill core logging. They respond to the need for the metallogenic mapping of mineral systems and the development of more robust mineral prospectivity maps and exploration strategies for the discovery of critical and precious metal resources in MIAC systems.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".