A Conceptual Approach to the Development of Digital Geological Field Data Collection
Bibliographic record
Abstract
Members of the Geological Survey of Canada (GSC) have for several years been successfully developing digi-tal systems that aid geologists in the capture of fi eld data. In the past, development has been completed, or driven, by an individual researcher on a per project basis and, therefore, systems have been specifi c to that geologist’s work. This sort of application development has often meant that the work takes place in virtual isolation and the resulting application can be very limited in scope or usability for other researchers. Due to the demands of business re-alignment in the GSC over the past few years, there has been an attempt to work toward a single system that could be used by a variety of researchers for the collection of fi eld informa-
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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 | 0.054 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.007 | 0.036 |
| Scholarly communication | 0.022 | 0.022 |
| Open science | 0.010 | 0.012 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".