Bibliographic record
Abstract
Geographic information systems not only suppty the tools for building and accessing a multi', map gioscientific database, they can also providc the computing platform for developing and testing mineral potential modets. In general, such modcls are algoithms for combining multi' pte tnput maps (geologt, geophysics, geochemistry) to produce an ouq)fi map of mineral potential, in some cases with an associated unceftainty map. In relatively well-explored regions, with an abundance of lonwn mineral showings, predictive mineral potential modelling can use either regression techniqtes or a new Bayesian method known as 'weights of evidence' modelling. For uannple, gold potential in the Meguma ter' rane, Nova Scotia has been mapped by combining weights of evidence from lithologl, distance to contads and stntctures, with lake-sediment and biogeochemical signatures. Areas where the estimate of mineral potential is uncertain orc maskcd out. The model not onty predicts all the important gold districts, but also shows when areos of high potential occur with no lcttown mineralization. For the Star Lake areg with too few sites of lonwn mineralization for statistical predictive modelling expert opinion can be used to Benerole a seies of tial models. The 'weights of evidence'framewoik can still be use4 but instead o/measu'ig the overlap relationships of Ionwn occunences with the predictor map6 the upeft is asked fo estimate these relationships, The modelting tanguage and interactive graphical display of the geographic information system facititate the uperimentation with a variety of modcls. These expeiments yield a seies of mineral potential maps; the tnaps are neither 'right' nor Tvrong', but simply products of the assumptions, similar to Chamberlin's multiple wo*ing hypotheses, and aid the e.xploration pnocess.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".