Does the river continuum concept apply on a tropical island? Longitudinal variation in a Puerto Rican stream
Bibliographic record
Abstract
We examined whether a tropical stream in Puerto Rico matched predictions of the river continuum concept (RCC) for macroinvertebrate functional feeding groups (FFGs). Sampling sites for macroinvertebrates, basal resources, and fishes ranged from headwaters to within 2.5 km of the fourth-order estuary. In a comparison with a model temperate system in which RCC predictions generally held, we used catchment area as a measure of stream size to examine truncated RCC predictions (i.e., cut off to correspond to the largest stream size sampled in Puerto Rico). Despite dominance of generalist freshwater shrimps, which use more than one feeding mode, RCC predictions held for scrapers, shredders, and predators. Collectorfilterers showed a trend opposite to that predicted by the RCC, but patterns in basal resources suggest that this is consistent with the central RCC theme: longitudinal distributions of FFGs follow longitudinal patterns in basal resources. Alternatively, the filterer pattern may be explained by fish predation affecting distributions of filter-feeding shrimp. Our results indicate that the RCC generally applies to running waters on tropical islands. However, additional theoretical and field studies across a broad array of stream types should examine whether the RCC needs to be refined to reflect the potential influence of top-down trophic controls on FFG distributions.
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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) | 0.001 | 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".