Bibliographic record
Abstract
Models of river plankton frequently suggest that these passively drifting communities are limited to downstream sections of larger rivers. I examine this hypothesis using a passive drift model for populations in advective environments, followed by a comparison of predicted and observed plankton populations in rivers. Under the scenario of continuous downstream drift, much of the plankton found in rivers is not predicted to occur. However, much of the observed plankton in rivers is explained through the addition of cross-channel flow heterogeneity to the model. Empirical data support the model and predict that many river plankton populations may be drifting downstream at less than 30% of the average rate of downstream flow. Plankton collections in the slower-moving edges of rivers demonstrate densities of up to 240% higher than those in adjacent midchannel flows (p = 0.009). These slow-moving areas are important habitat for river plankton and likely play a large role in planktonic food webs within rivers. These results may help explain why river productivity often decreases as a result of the loss of flow heterogeneity within river channels through human modification to landscapes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".