Remote Predictive Mapping 1. Remote Predictive Mapping (RPM): A Strategy for Geological Mapping of Canada’s North
Bibliographic record
Abstract
Remote Predictive Mapping (RPM) techniques are being developed and refined by the Geological Survey of Canada for mapping Canada’s North. Remote Predictive Mapping should be considered an integral part of the geological mapping process designed to involve compilation, and re-compilation of data derived from existing geological maps, aerial photographs, satellite imagery, and airborne geophysical data. Predictive geological maps may be iteratively revised and upgraded to publishable geological maps by integrating remotely sensed data with newly acquired field and laboratory data, as RPM techniques are progressively tested and insight evolves. A predictive map, produced without collection of new, field-based data, may also serve as a first-order geologic map in areas where field-based studies cannot be accomplished due to expense of field access or remoteness. As a welcome consequence of adopting RPM into the normal work flow of any mapping or exploration project, there will, by necessity, be greater participation and integration of expertise of field geologists, geophysicists, Geographic Information System (GIS) and remote sensing specialists. Significantly, RPM also encourages geoscience organizations to make full use of all available geoscience data. This paper outlines a strategy for RPM and provides processing and interpretation examples based on a variety of geoscience data and interpretation techniques to be employed for geologic mapping. SOMMAIRE La Commission geologique du Canada developpe et raffine des techniques de telecartographique predictive (TCP) pour cartographier du Nord canadien. La telecartographie predictive doit etre percue comme une composante integree d’un processus de cartographie geologique de compilation et de recompilation de donnees extraites de cartes geologiques, de photographies aeriennes, d’imageries satellitaires, et de geophysiques aeroportees existantes. Les cartes geologiques predictives peu-vent ainsi etre revisees, mises a jour et publiees selon une approche iterative integrant les donnees de teledetection avec les donnees de terrain et de laboratoire nouvellement acquises, au gre de l’evolution et du raffinement des techniques de TCP. Dans les cas de regions trop eloignees, ou parce que les couts d’etablissement de cartes geologiques de base regulieres seraient prohibitifs, la TCP peut aussi etre utilisee pour produire une carte geologique de base. D’entree de jeu, on realise que l’adoption de la TCP dans la routine de production normale de tout projet de cartographie ou d’exploration permettra, en soi, une meilleure prise en compte et une meilleure integration des savoirs-faires des geologues de terrain, des geophysiciens et des specialistes de la teledetection et des systemes d’information geographique (SIG). Par sa nature meme, la TCP permet aux organisations geoscientifiques de faire plein usage de toutes les donnees geoscientifiques dont elles disposent. Le present article definit une strategie de TCP et decrit des exemples de traitement et d’interpretation d’une variete de donnees geoscientifiques et de techniques d’interpretation utilisables pour la production de cartes geologiques.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".