Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.008 |
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 it