Analyse de l'information améliorée offerte par les cartes géologiques publiées par le gouvernement aux fins de l'exploration minérale : intégration de considérations sur l'efficience, la productivité, l'efficacité et lerisque
Bibliographic record
Abstract
This bulletin focuses on the value of geoscientific information and knowledge, as provided in published government bedrock geological maps, to the mineral exploration sector. An economic model is developed that uses an attribute-ranking approach to convert geological maps into domains of mineral favourability. Information about known deposits in these (or analogous) favourability domains allow the calculation of exploration search statistics which provide input into measures of exploration efficiency, productivity, effectiveness, risk, and cost stemming from the use of the published geological maps. Two case studies, the Flin Flon Belt (Manitoba and Saskatchewan) and the south Baffin Island area (Nunavut), demonstrate that updated, finer resolution maps provide more detailed and accurate information than old, coarser resolution maps when used as a guide for mineral exploration. The Flin Flon Belt study illustrates that an updated, coarser resolution bedrock map enables improved mineral exploration efficiency, productivity, and effectiveness by locating 60% more targets and supporting an exploration campaign that is 44% more efficient. Refining the map resolution provides an additional 17% reduction in search effort across all favourable domains and a 55% reduction in search effort in the most favourable domain. The south Baffin Island case study projects a 40% increase in expected targets and a 27% reduction in search effortwhen the updated, finer resolution map is used in lieu of the old, coarser resolution map. On southern Baffin Island, the economic value of the updated map ranges from CAN$2.28 million to CAN$15.21 million, which can be compared to the CAN$1.86 million that it cost to produce the map (a multiplier effect of up to eight).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".