Bibliographic record
Abstract
La rarete de certaines ressources naturelles devient un probleme geopolitique majeur a mesure que croit la population mondiale. Le petrole en est, depuis quelques decennies, l’exemple type, mais l’eau s’affirme peu a peu comme un ferment de conflits a venir. Longtemps partagee de facon plus ou moins empirique, l’eau des lacs et des fleuves, determinante pour l’agriculture, est devenue un enjeu crucial dans plusieurs regions du monde. L’assechement de la mer d’Aral et du fleuve Colorado sont des catastrophes ecologiques majeures, tandis que le controle des eaux du Nil par l’Egypte aux depens de l’Ethiopie, de l’Euphrate par la Syrie ou du Jourdain par Israel a eu, et aura encore, des consequences politiques redoutables, que le rechauffement climatique en cours ne pourra qu’accentuer. Ressource strategique et ecologique majeure, l’eau implique une perception nouvelle : l’urbanisme actuel fait tout – a Londres comme a Paris – pour reconcilier la ville avec son fleuve. Puisse cette demarche inspirer la diplomatie des Etats contraints a partager cet indispensable bien commun.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.037 | 0.006 |
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".