Glacial dispersal IM plume library, data entry reference manual
Bibliographic record
Abstract
Much of Canada has been sampled for indicator minerals to support mineral exploration activities. The application of indicator mineral techniques for exploration in glaciated terrains accelerated through the 1980s and 1990s as the potential for diamond deposits in the Canadian Shield attained broader appreciation and exploration success increased. Following the 1991 discovery of the Ekati deposit in the Lac de Gras area interest in indicator minerals increased enormously and there was an increased recognition of the need to better understand indicator mineral distribution in glacial deposits. Subsequently there have been a number of notable publicly funded initiatives to support centralized indicator mineral databases (e.g. Kimberlite Indicator Diamond Database - KIDD). There has also been an increased interest in applying indicator mineral techniques to the search for other commodities. Nevertheless, integration of the controls that glacial processes exert on indicator mineral dispersion remains poorly developed. To address this issue the subproject on indicator minerals within the Target Geoscience Initiative 4 (TGI-4) is developing an Indicator Mineral Dispersal Atlas for selected regions and dispersal datasets in Canada. A preliminary activity has been to compile the published literature on indicator minerals and develop a library (database) of reference material for integration with other geoscience datasets. This report documents the structure and workflow developed for data entry and export from this library that uses Microsoft Access, Google Earth, and ArcInfo.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.267 | 0.233 |
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".