Enjeux économiques : quel est le potentiel des ressources minérales marines ?
Bibliographic record
Abstract
Les besoins en ressources minérales vont croissants. La place occupée par des technologies toujours plus consommatrices de métaux oblige à s’interroger sur les potentialités de l’exploitation minière sous-marine, qui est aujourd’hui limitée à quelques cas se situant à proximité des côtes et au projet emblématique – Solwara 1 – de l’entreprise canadienne Nautilus, en Papouasie-Nouvelle-Guinée. Les conditions d’exploitation demeurent globalement incertaines et trois questions restent sans réponse claire : a) la nature des technologies nécessaires à cette exploitation, b) l’évaluation du coût financier de telles opérations et, enfin, c) l’impact sur l’environnement de l’exploitation de mines sous-marines. Le potentiel est élevé, mais ces obstacles rendent éminemment aléatoire toute prédiction future sur ce sujet. Consciente toutefois de son intérêt, la France a approuvé en octobre 2015, en Comité interministériel de la mer, une stratégie nationale relative à l’exploration et à l’exploitation minières des grands fonds marins.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".