Detecting Labrador Sea Water formation from space
Bibliographic record
Abstract
In situ monitoring of deep water formation in the Labrador Sea is severely hampered by the harsh winter conditions in this area. Furthermore, the ongoing monitoring programs do not cover the entire Labrador Sea and are often summer observations. The network of satellite altimeters does not suffer from these limitations and could therefore give valuable additional information. Altimeters can in theory detect deep water formation, because the water column becomes denser during convection and therefore the sea surface becomes lower. This signal is small compared to variability in sea surface height induced by other sources, but when properly filtered and appropriately averaged in time and space, all four winters with Labrador Sea Water formation or renewal in the 1994–2009 period (1994, 1995, 2000, and 2008) have a clear large negative anomaly. The magnitude of this anomaly compares favorably with the range predicted by theory and in situ data analysis. Out of 16 winters, only one winter (2006) would be falsely identified as a deep convection winter based on its sea surface height anomaly signal, while the method did not miss a single deep convection winter. For most deep‒water‒formation winters even the spatial structure of the mixed layer depth distribution can be inferred.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".