Patterns in fish species composition across the interface between streams and lakes
Bibliographic record
Abstract
We compared fish species compositions among four site types (lake, lake mouth, stream mouth, stream) along the gradient from stream to lake for 12 tributary mouths. Comparison of species richness, rarefaction species diversity, and species density all supported the same pattern: stream-mouth sites contained the highest number of species, followed by stream sites, lake-mouth sites, and lake sites, even though lake and lake-mouth sites yielded more individuals and were larger in area and volume. Principal components analysis formed three clusters of mixed sites based on similarities in dominant fish species rather than designations of lake, lake mouth, etc. Rank order assemblage tests revealed that species composition of tributary-mouth sites was similar in only one quarter of the systems sampled; other systems showed a transition from "lake" to "stream" species compositions at or near the tributary mouth. Species assemblage comparisons within site types between systems revealed low consistency in the composition of stream-mouth sites and high consistency for the other site types. We concluded that the tributary mouth fits the definition of an ecotone and speculate that the difference in hydrologic and geomorphic properties of streams and lakes played a role in the patterns that we saw on either side of the tributary mouth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".