Chemical and biological oceanographic conditions in the Labrador Sea from 2019 to 2023
Bibliographic record
Abstract
The Atlantic Zone Off-Shelf Monitoring Program samples the AR7W line annually. This report summarises trends from 2019-2023 for three regions: AR7W-W (Labrador shelf and slope), AR7W-C (central Labrador Sea), and AR7W-E (Greenland shelf and slope). Samples revealed a continued increase in dissolved inorganic carbon and a decrease in pH from 2019 to 2023. Mean concentration of CFC-12 decreased in 2020, and SF6 continued its steady increase. Mean temperature from 0-100 m in the Labrador Sea was above normal in 2019, below normal on the next mission (2022), and near or above normal in 2023. Surface (0-100 m) nutrients were mainly below normal from 2019-2023, which could be attributed to mission timing. However, below-average deep nutrients (>100 m, less impacted by sampling timing) suggests a profound change in the biogeochemistry of the Labrador Sea. Integrated (0-100 m) chlorophyll-a was below normal in 2019 and in AR7W-E in 2022-2023, but above normal elsewhere, with a record high value in AR7W-C in 2022 caused by an unusually large bloom of Phaeocystis spp.. Satellite data revealed high variability in the timing of the spring and fall blooms and surface average chlorophyll-a concentration. Mesozooplankton abundances showed high interannual variability since 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 0.001 |
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".