Acquiring reusable location data for stratigraphic studies: insights from the Central Foreland NATMAP Project
Bibliographic record
Abstract
Stratigraphic data that can be used by subsequent investigators without the need to revisit a site are reusable data. Accurate location data are a crucial part of ensuring reusability of data, and thus contribute to the reproducibility of scientific studies and to cost savings.Areview of some outcrop stratigraphic location data collected during the GSC's Central Foreland NATMAP Project found several categories of data errors that hamper reusability. These include errors in recording map datum, inaccurate co-ordinates, typographic errors introduced into co-ordinates, and errors in map presentation. Project leaders should ensure that all project participants who study stratigraphic sections are aware of the benefits of reusability and that participants follow specific criteria for recording location data; if required, they should provide participants with training in both traditional map craft and emerging technologies.Ashort time invested in capturing accurate location data on the section translates directly into significant time savings during report preparation.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| 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".