Operation Treasure Hunt – Does the Ontario model work for you?
Bibliographic record
Abstract
The Ontario Geological Survey is midway through a three-year, C$29 million initiative known as Operation Treasure Hunt. Its main objectives are to collect and disseminate geophysical, geochemical and geological data to industry, to identify exploration targets to attract investment in mineral exploration of the province. The geophysical component in the first two years has included the acquisition of nearly 140,000 line-km of magnetic-electromagnetic data over eight survey areas, and the purchase of an additional 105,000 line-km of proprietary data from industry. The Reid-Mahaffy airborne electromagnetic test range was established to facilitate comparison of systems for a variety of geological targets, and has been rapidly adopted by industry. Early impact analysis has shown that the imminent release of geophysical and geochemical data in strategically chosen areas results in a significant increase in claim staking activity and subsequent exploration expenditures in an area. A twinning agreement with the Geological Survey of New South Wales and discussions with other Australian state agencies has allowed the Ontario Geological Survey to optimise its program based on the Australian experience, while adapting it to the local geological and jurisdictional conditions.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.055 | 0.017 |
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".