Bibliographic record
Abstract
David Bond is a professor of palaeoenvironments at the University of Hull, UK.He has been lucky enough to travel to >30 countries over the past 20 years to collect rocks and fossils that help him and his collaborators understand what drove some of the greatest biotic catastrophes of the past ~444 mil lion years.His recent research has focused on two Permian catastrophes around 8 million years apart-an interval of extremes of climate, extinction, and evolution.In particular, he has been exploring the volcanismextinction link in the Boreal Realm of northern high latitudes with several excursions to the Canadian and Russian Arctic and Svalbard.Sara Callegaro is a researcher in igneous petrology and geochemistry at the University of Oslo, Norway, which she joined in 2016.She has been working on LIPs since her PhD (2012) at the University of Padova, Italy.Initially, her research focused mostly on tracking the mantle source and petrogenesis of LIP basalts through radiogenic isotope geochem istry.More recently, she has been working on intru sive rocks and magma-host rock interaction and on characterizing volcanogenic and thermogenic degassing from volcanic basins on sev eral LIPs, including the Central Atlantic Magmatic Province, Siberian Traps, and Karoo.She is a firm believer that fieldwork observation is essential in geoscience.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.578 | 0.388 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".