Phytoplankton species turnover controlled by physical forcing at different time scales
Bibliographic record
Abstract
Changes in the physical and chemical environment induced by forcing events such as wind and rain can control the short-term dynamics of the phytoplankton community of inland waters. In two temperate reservoirs, species composition, diversity index, and species dynamics were characterized over periods of 3 weeks during different seasons, in parallel with changes in the physical and chemical structure of the water column. Dominant species and diversity index were shown to be controlled by environmental factors fluctuating on a seasonal and weekly time scale, whereas some rarer species responded to wind events and were indicators of a very short-term environmental change. As a consequence of the response of indicator species, the rate of change in species composition increased following meteorological events. Shifts in species composition were observed during each sampling period, except during the strongest stratification period. The shifts were associated with a modification of resource availability or intensity of stratification. We found that the resistance of the phytoplankton community to short-term physical forcing by wind increased with intensity of stratification. After the forcing event, the community did not return to its original structure because of modifications of the physical and chemical conditions induced by meteorological forcing on a weekly time scale.
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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) | 0.000 | 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".