Long-Term Relationships between the Marine Environment, Krill and Salps in the Southern Ocean
Bibliographic record
Abstract
Long-term variations (1975–2002) in climatology of marine environmental parameters, Antarctic krill,Euphausia superba, and the pelagic tunicate,Salpa thompsoni, were compared within the Atlantic Sector of the Southern Ocean. Sea water temperature in the top 400 m increased at a rate of 0.020–0.030°C ⋅ yr−1, which was accompanied by the dissolved oxygen decline. Top 100 m water layer became fresher with lower concentrations of phosphates and nitrates, while at subsurface layers (200–400 m) both salinity and nutrients showed small increasing trend. Unlike phosphates and nitrates, silicate concentrations decreased in the entire water column. Shorter-term water temperature dynamics closely correlated with the El Nino events expressed as the Southern Oscillation Index which in turn was linked to the propagation of the Antarctic Circumpolar Wave (ACW). The variations of sea-ice extent matched well the changes in both air and water temperatures. In general, abundance of krill and salps showed opposite to each other trends. Due to large area considered in this study, no significant relationships between abiotic factors and both krill and salps were found. However, our analysis demonstrated that krill abundance was greater in years with lower sea water temperature, greater sea-ice extent and higher nutrient concentration, while salps showed the opposite pattern.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".