La navigation en arctique en 2021 : le moteur des ressources extractives
Bibliographic record
Abstract
La pandémie de Covid-19 qui a frappé les sociétés à partir de mars 2020 a provoqué un \nralentissement économique considérable pendant plusieurs mois. En Arctique, la navigation en a été affectée, mais de manière inégale. \nSi le trafic dans les eaux groenlandaises a ainsi considérablement chuté, dans l’Arctique \ncanadien le ralentissement semble avoir été modéré, tandis que la croissance du trafic se \npoursuit dans l’Arctique russe. Quels sont les moteurs de cette résilience, et quel est le portrait \npost-pandémie du trafic maritime arctique ? \nUN ACCROISSEMENT RÉEL DE LA NAVIGATION DANS L’ARCTIQUE \nLes chiffres soulignent que si les mouvements de navires sont en nette augmentation dans \nl'Arctique, le portrait par région offre une image plus nuancée. De 2009 à 2019, le trafic a été \nmultiplié par 1,92 dans l'Arctique canadien ; de 1,97 dans les eaux groenlandaises ; de 1,58 \nentre 2016 et 2019 dans les eaux de la Route maritime du Nord.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.009 |
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".