Food or physics: plankton communities structured across Gulf of Alaska eddies
Bibliographic record
Abstract
Abstract Oceanic features, such as mesoscale eddies that entrap and transport water masses, create heterogeneous seascapes to which biological communities may respond. To date, however, our understanding of how internal eddy dynamics influence plankton community structuring is limited by sparse sampling of eddies and their associated biotic communities. In this paper, we used 10 years of archived Continuous Plankton Recorder (CPR) data (2002-2013) associated with 9 mesoscale eddies in the Northeast Pacific/Gulf of Alaska to test the hypothesis that eddy origin and rotational direction determines the structure and dynamics of entrained plankton communities. Using generalized additive models and accounting for confounding factors (e.g., timing of sampling), we found peak diatom abundance within both cyclonic and anticyclonic eddies near the eddy edge. Zooplankton abundances, however, varied with distance to the eddy center/edge by rotational type and eddy life stage, and differed by taxonomic group. For example, the greatest abundance of small copepods was found near the center of anticyclonic eddies during eddy maturation and decay, but near the edge of cyclonic eddies during eddy formation and intensification. Distributions of copepod abundances across eddy surfaces were not mediated by phytoplankton distribution. Our results therefore suggest that physical mechanisms such as internal eddy dynamics exert a direct impact on the structure of zooplankton communities rather than indirect mechanisms involving potential food resources.
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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 | 0.000 |
| 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) | 0.002 | 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".