Towards understanding the relationship between wind conditions and plankton patchiness
Bibliographic record
Abstract
We used (1) water temperature (a proxy for water movement), (2) chlorophyll (a proxy for phytoplankton), and (3) zooplankton (bulk, small, and large size classes) to investigate the relationship between changing wind conditions and spatial patterns along linear transects (n 5 150) in two basins, South Arm and Annie Bay, of Lake Opeongo (Ontario, Canada). The basins have similar biological characteristics, but South Arm is larger and is oriented along the prevailing westerly wind direction. Large‐scale patterns (.1 km) were described with an accumulation index, and wavelet analysis was used to describe small‐scale patterns (≪1 km). Spatial descriptors were correlated with five descriptors of wind conditions: (1) wind force, (2) scalar wind speed, (3) vector wind speed, (4) wind persistence, and (5) wind direction. Persistent westerly winds in South Arm resulted in more downwind accumulation of warm water and total and large‐bodied zooplankton than in Annie Bay, while chlorophyll and small zooplankton did not show consistent downwind accumulation. The predominance of small‐scale variability, particularly in large zooplankton, increased in the South Arm as the persistence and strength of westerly winds blowing parallel to the sampling transects increased. Only temperature showed such a pattern in the smaller Annie Bay. These patterns were not related to winds blowing at the time of sampling but rather to those blowing up to 12 h before sampling. Our observations provide a basis for future consideration of how simple surface winds may actually shape the nature of trophic interactions in lake ecosystems.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".