Evaluating the effect of wind‐driven patchiness on trophic interactions between zooplankton and phytoplankton
Bibliographic record
Abstract
We measured spatial patterns of zooplankton and chlorophyll concentration (a proxy for phytoplankton) with continuous sensors along horizontal transects that were repeatedly sampled (n = 150) under varying wind conditions throughout a growing season in two basins (South Arm and Annie Bay) of Lake Opeongo, Ontario, Canada. Spatially explicit in situ simulations that included activity costs associated with feeding were used to examine the effects of chlorophyll patchiness on the energy gain in different zooplankton communities. Simulations were repeated for several zooplankton size classes (small, large, and bulk) and two communities (all copepods and all cladocerans). For each simulated combination, a spatial energetic differential (SED) was estimated by contrasting the energy that zooplankton could gain using observed spatial patterns in chlorophyll and water temperature with the energy they could gain using uniform concentrations of chlorophyll and water temperature. Large zooplankton showed the greatest SED range across all communities, from a decrease of 8% to a maximum increase of 20%, assuming relatively low costs associated with feeding activity. Small zooplankton had the narrowest SED range. Zooplankton energy gain is sensitive to both the degree of zooplankton‐chlorophyll spatial overlap and energetic costs associated with zooplankton feeding activity. SED values as high as 485% can occur under plausible estimates of activity costs. Wind‐driven increases in spatial overlap between predator and prey can be large enough to substantially alter planktonic trophic interactions.
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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 | 0.002 |
| 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".