GOHSnap: Updates from the field
Bibliographic record
Abstract
Over the last century, the ocean has become steadily more depleted in oxygen while also absorbing about 25% of the anthropogenic carbon dioxide added to the atmosphere each year. Therefore, observing gas uptake and transport processes is essential for understanding and predicting the evolution of the ocean and climate system. While it is often assumed that a slowdown in the AMOC will lead to deoxygenation of the deep ocean and a decrease in the uptake and storage of carbon, we have never had observations sufficient to test these assumptions. In this talk, I will describe a collaborative project called Gases in the Overturning and Horizontal circulation of the Subpolar North Atlantic Program (GOHSNAP), in which we deployed oxygen sensors over the full depth of OSNAP moorings in the Labrador Current and on both sides of Cape Farewell, Greenland, and pCO2 sensors at few key locations near the ocean's surface. We are using these additions to the moored array to investigate the rates and processes governing gas exchange and separate the role of the overturning and horizontal circulation on the export of gases from the Labrador Sea. The first gas sensor data from this program was recovered in summer of 2022, so the emphasis of this talk will be on the hydrographic data that led to GOHSNAP's hypotheses, tips and tricks for collecting oxygen data on moorings, and a sneak peek of preliminary results.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.263 | 0.226 |
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".