Sedimentary fluxes and budgets in changing cold environments: the global iag/aig sediment budgets in cold environments (sedibud) programme
Bibliographic record
Abstract
1Geological Survey of Norway (NGU), Quaternary Geology and Climate group, Trondheim, Norway 2Norwegian University of Science and Technology (NTNU), Department of Geography, Trondheim, Norway 3Queen’s University, Department of Geography, Kingston, Canada 4University of Clermont-Ferrand, Laboratory of Physical and Environmental Geography GEOLAB, CNRS, Clermont-Ferrand, France 5Natural Research Centre of North-western Iceland, Saudarkrokur, Iceland 6University of Arkansas, Department of Geosciences, Fayetteville AR, USA 7 University of Otago, Department of Geography, Dunedin, New Zealand 8University of Salzburg, Department of Geography and Geology, Salzburg, Austria 9University of Colorado, Institute of Arctic and Alpine Research, Boulder, CO, USA 10Durham University, Department of Geography, Durham, UK 11Adam Mickiewicz University, Institute of Paleogeography and Geoecology, Poznan, Poland
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".