Technicalities: Exploring the Labrador sea with autonomous vehicles
Bibliographic record
Abstract
The Labrador Sea is a fascinating and difficult environment in which to work. In the winter, wind speeds can gust upwards of 200 km/hr, while 10-m wave heights and below freezing temperatures (-20°C) are not unheard off, making it an inhospitable area for field work. Indeed, few ships are present in the Labrador Sea during the winter. However, the same harsh conditions have made the Labrador Sea a key region for Earth’s climate, with the wintertime conditions resulting in localized deep mixing of waters and carbon to great depths (2 km) in the ocean [Lazier, 1980; Pickart, 1997]. As a consequence, in-situ observations in the Labrador Sea are critical to advancing scientific knowledge on past and future climate change scenarios. Previous attempts to use ships for wintertime work required long expeditions at sea, but often with little data collected due to unworkable conditions. Autonomous marine vehicles provide an obvious solution to collecting in-situ data in the wintertime, as they can operate in extreme conditions yet still give us the flexibility to adapt our sampling during the mission [deYoung et al., 2018; Testor et al., 2019].
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".