Effects of wind on lake plankton patchiness and trophic interactions
Bibliographic record
Abstract
Over thirty years of research has shown that zooplankton and phytoplankton exist in patches generated by physical (e.g., water movement caused by wind) and biological processes (e.g., vertical migration). These patches constantly change in response to weather conditions, however the trophic effects of this have not been thoroughly evaluated because previous studies have included relatively few transects. In this study, 150 sampling transects were collected from two basins (South Arm and Annie Bay) of Lake Opeongo (ON, Canada) under varying wind conditions. While the basins are biologically similar, South Arm is more exposed to the prevailing westerly winds, due to its size and orientation. On each transect, water temperature, chlorophyll concentration (proxy for phytoplankton) and zooplankton size were simultaneously recorded with a spatial resolution of 1.5m. The spatial patterns of each recorded variable were described at a wide range of scales using wavelet analysis, since spectral analysis was inappropriate due to the presence of large-scale trends and abrupt changes in variability. Statistically significant changes in variability were detected in all of the variables and the locations of these changes were mapped. The results showed that change points were relatively uniformly distributed along the sheltered Annie Bay transects, but were more concentrated in the most exposed region of the South Arm transects. Changes in wind conditions were correlated with the spatial patterns of the recorded variables. The results showed that large-scale downwind accumulations were more frequent in South Arm, where winds were generally stronger and more persistent. Small-scale variability was associated with strong winds in both basins. Carbon-budget simulations were used to investigate the link between wind-driven spatial patchiness and trophic interactions. Spatially-explicit budgets evaluated zooplankton growth potential against uniform conditions, which assumed median values of temperature and chlorophyll concentration. Regardless of basin, a growth advantage was observed more frequently than a disadvantage. However, statistical models show that the basins had different spatial patterns of chlorophyll concentration and wind conditions responsible for generating this growth advantage. The results demonstrate that observed spatial patterns should be incorporated into carbon budget simulations.
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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) | 0.001 | 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".