Effects of temperature and ultraviolet radiation on diel vertical migration of freshwater crustacean zooplankton
Bibliographic record
Abstract
In transparent, thermally stratified lakes, ultraviolet radiation (UV) and temperature are among the primary factors influencing diel vertical migration (DVM) of zooplankton. It is not well known how behavioral responses will vary across a wide range of depths with steep gradients in UV, photosynthetically active radiation (PAR), and temperature. Our objective was to determine the relative importance of UV and temperature on DVM of Daphnia catawba and Leptodiaptomus minutus in a high UV, thermally stratified lake. Using UV-transparent (+UV) and UV-opaque (–UV) columns suspended within the mixed layer and metalimnion, we found that both species were generally shallower in the –UV than in the +UV columns. Daphnia catawba responded negatively to UV, even below the 1% depth for 320 nm, whereas L. minutus responded to UV only within the mixed layer and upper metalimnion. Daphnia catawba did not migrate in the –UV in the mixed layer, but migrated upwards in the –UV in the deep part of the metalimnion, indicating a temperature-induced rather than phototactic response. At night, both species migrated upwards in the metalimnion and were evenly distributed in the mixed layer. These results indicate that in transparent lakes, UV may constrain some zooplankton to cooler, suboptimal temperatures, which may compromise fitness.
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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 | 0.000 | 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".