Grazercollector facilitation hypothesis supported by laboratory but not field experiments
Bibliographic record
Abstract
Grazing invertebrates in streams feed by harvesting algal cells from surfaces, and in doing so release fine particulate organic matter (FPOM). The "grazercollector facilitation hypothesis" holds that FPOM production by grazers facilitates growth and (or) survival of FPOM-collecting invertebrates. We tested for grazercollector facilitation in laboratory and field experiments. In recirculating flumes in the laboratory, we tested for facilitation of the collector Hydropsyche slossonae by the grazers Physa gyrina, Glossosoma intermedium, and Baetis tricaudatus. All three grazers increased FPOM levels in flume water, but only Physa facilitated Hydropsyche growth. In the field, we manipulated Physa and Glossosoma densities to test for facilitation (at a local scale) of natural collector assemblages in an eastern Iowa stream. We did not detect facilitation of any collector by either grazer in the field, despite high power to detect such interactions. We suspect that grazercollector facilitation was not observed in the field because (unlike in our laboratory flumes) field FPOM levels are often high and extremely variable in time and space and because organic particles can arise from sources other than grazer activity (= grazer-independent processing). Therefore, at local scales, collectors may not be significantly limited by the supply of grazer-derived FPOM.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".