Bibliographic record
Abstract
The areas that we studied in the North Atlantic (53 and 60°N) and in the Labrador Sea in the summer were characterized by a wide variability of the concentrations of dissolved and particulate organic matter and its elemental composition both in the surface and in the deep waters. The concentrations of dissolved and particulate C org varied within 69–360 μM and 0.7–25.6 μM, respectively; the N org and P org contents varied within 1.4–22.2 μM and 0.02–0.86 μM, respectively. The maximal concentrations were registered in the photic layer and in the zones of mixing between the waters of different genesis. The particulate matter contribution to the total organic matter (OM) content varied from 0.5 to 15.4%. The waters of the photic layer contained more particulate C org than those of the near-bottom layer. The values of the C/N molar ratios from the surface to the bottom over the entire aquatic area surveyed varied 5-to 6-fold; at that, the values of the C/P molar ratios varied more than tenfold. In the most productive waters, the values of the C/N ratios were close to the Redfield ratios (6–10). The values of the C/P molar ratios varied from 160 in the photic layer to 4831 in the deep waters. The pronounced non-uniformity in the spatial distribution of the OM and its elemental composition is caused not only by the penetration of the waters of different origins but also by the changes in the microplankton metabolism under mixing of these waters.
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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 | 0.001 | 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 | 0.000 |
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".