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.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads agree on what is shown here.
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".