Supplementing public geoscience knowledge with archived industry data: an example from northwest Canada
Bibliographic record
Abstract
Mineral industry exploration data archived in the public domain are an underutilized source of geoscience knowledge in Canada. Efforts to rescue archived data can contribute to the repository of public geoscience knowledge, thereby meeting the objectives of the Government of Canada's Open Data Initiative and increasing the cost effectiveness of modern mapping projects. This report documents a data rescue exercise dealing with a high quality set of maps produced by a 1975 Rio Tinto exploration program in the Mackenzie Mountains, NWT, (RT claim group, northern NTS 106B and southernmost NTS 106G) and now residing in the public domain. These maps were digitized in preparation for Geological Survey of Canada (GSC) field work in the Mackenzie Mountains. Line-work, lithological information, and structural data were captured in a GIS database. These archival maps cover a small area in much greater detail than was possible for the regional maps published by the GSC. As a result, the digitized dataset will allow for strategic selection of sites requiring a revisit, potentially streamlining the GSC's present and future mapping campaigns in the region. The high density of structural measurements contributed to clearer understanding of map unit distribution during GSC field work in 2016, and may contribute to a detailed structural analysis of the region. The 1975 Rio Tinto exploration program cost over $450,000 CAD ($2 million in 2016 dollars). The cost of rescuing the data was minor by comparison, consisting mainly of the salary required to pay a junior scientist to carry out the digitizing.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.021 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".