Cladoceran body length and Secchi disk transparency in northeastern U.S. lakes
Bibliographic record
Abstract
Mean cladoceran body length of 59 northeastern U.S. lakes was estimated from functional groups that broadly define taxonomic, body size, and grazing potential. Multiple regression of body length, color, and chlorophyll a or total phosphorus against Secchi disk transparency explained 72% and 83% of the variation across lakes, respectively. Analysis that included body length, color, and particulate carbon, a proxy for light backscattering and absorption by suspended organic particles, explained 85% of the variance in transparency. Body length was as important a predictor of water clarity as chemical factors. Furthermore, body length was significantly correlated to temporal variation in transparency within lakes. Because cladocerans primarily filter organic particles in size ranges having high light attenuation efficiencies, body length was consistently more strongly correlated to transparency than to chlorophyll a. Monitoring cladoceran body length should help to distinguish changes in lake transparency due to nutrient loading from changes that reflect fish population size structure and predation intensity on zooplankton. This simple size index can greatly increase the interpretative value of Secchi transparency observations to lake managers.
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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 | 0.001 | 0.001 |
| 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".