Maximizing the value of historical bedrock field observations: An example from northwest Canada
Bibliographic record
Abstract
Historical bedrock field observations have potential for significant value to the scientific community and the public if they can be rescued from physical records stored in archives of scientific research institutions. A set of historical records from ‘Operation Norman’, a bedrock mapping activity conducted in northwestern Canada by the Geological Survey of Canada (GSC) from 1968 to 1970, was identified as suitable for data rescue and incorporation into a GIS geodatabase. These observational data, including field stations, lithology descriptions, structural measurements, measured section locations, and fossil localities, were digitized as geospatial features with attributes assigned according to the observation records. Over 90% of the original observations were successfully rescued in this manner, allowing for effective incorporation with newer observations. Lack of reliable location information for field observations was the primary impediment to effective data rescue. Access to original participants in Operation Norman was particularly helpful in ensuring successful data rescue, as was the excellent state in which research materials had been curated. The resulting dataset of combined historical and recent observations provides improved distribution of observations to constrain geological analysis and map interpretation. Rescued data from Operation Norman have been incorporated in new bedrock map compilations and other scientific publications.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.012 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".