Exploratory data analysis of the interactions among physics, food web structure, and function in two Arctic polynyas
Bibliographic record
Abstract
Polynyas are areas of open water in ice-covered seas, characterized by high biological productivity. The NEW (Northeast Water) and NOW (North Water) polynyas, located off the east and west coasts of Greenland, were extensively sampled in 1993 and 1998, respectively. We used principal component analysis to explore the seasonal covariations among physical, chemical, and ecological characteristics in these polynyas. In both polynyas, the most explanatory eigenvector revealed the expected seasonal development of the bloom (nutrient consumption and biomass increases) associated with declining ice cover, warming, and mixed-layer shallowing. This seasonal pattern, however, was much weaker in NEW than in NOW. No connections between the structure of the food web and recycling processes were apparent in either polynya. The analysis points to much stronger and richer interactions between the mesoplankton (i.e., diatoms–zooplankton) and microbial food webs in NOW than in NEW. The differences between the polynyas may be explained in part by differences in their longevity, with the longer-lived NOW polynya having more time to develop complex trophic interactions. The results also indicate that the connections between food web structure and ecosystem function (i.e., new production versus recycling), at least at the seasonal scale, are weaker than expected.
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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 | 0.001 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".