The Meteoritical Bulletin, No. 105
Bibliographic record
Abstract
Abstract Meteoritical Bulletin 105 contains 2666 meteorites including 12 falls (Aouinet Legraa, Banma, Buritizal, Ejby, Kamargaon, Moshampa, Mount Blanco, Murrili, Osceola, Sariçiçek, Sidi Ali Ou Azza, Stubenberg), with 2244 ordinary chondrites, 142 HED achondrites, 116 carbonaceous chondrites, 37 Lunar meteorites, 20 enstatite chondrites, 20 iron meteorites, 20 ureilites, 19 Martian meteorites, 12 Rumuruti chondrites, 10 primitive achondrites, 9 mesosiderites, 5 angrites, 4 pallasites, 4 ungrouped achondrites, 2 ungrouped chondrites, 1 enstatite achondrite, and 1 relict meteorite, and with 1545 from Antarctica, 686 from Africa, 245 from Asia, 147 from South America, 22 from North America, 14 from Europe, 5 from Oceania, 1 from unknown origin. Note: 5 meteorites from Russia were counted as European. It also includes a list of approved new Dense Collection Areas and a nomenclature of the Aletai (IIIE‐an) iron meteorites from Xinjiang, China.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.108 | 0.100 |
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".