Bibliographic record
Abstract
Nous vivons dans un climat qui est defini comme tempere, c’est-a-dire qu’il change chaque heure du jour et d’une journee a l’autre. Pres de l’equateur, c’est different. J’ai vecu aux Philippines ou la temperature varie peu autour des vingt-six degres Celsius, sauf lors des typhons. Notre planete offre tous les climats possibles, entre les deux poles si froids. Chaque annee, l’hemisphere nord a ses saisons en opposition avec l’hemisphere sud. Chaque jour, notre midi correspond au minuit de la Chine et l’inverse. Bref, le climat change tout le temps sur notre planete bleue. Alors, pourquoi sommes-nous si desireux de lutter contre ces changements climatiques? Pourquoi ce sujet est-il devenu la principale motivation pour l’election du parti politique qui veut gouverner notre pays? Pourquoi croyons-nous que le climat change tellement que nous devions tout mettre en œuvre pour arreter ces changements? La theorie du professeur Lu de l’Universite Waterloo ON Canada repond a ces interrogations.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".