Substrate as a correlate of density and distribution of larval sea lampreys in streams
Bibliographic record
Abstract
I investigated larval habitat of the sea lamprey, 'Petromyzon marinus', comparing season- and age-specific distributional differences in two Lake Ontario tributaries. Adaptive cluster sampling, which I selected to address spatial clumping of larvae, required 2.4 times more effort than initial random sampling, but increased capture by 64%, and improved mean C.V. by nearly 3-fold. Zero catches, although reduced from 68% to 42%, remained problematic for analysis. I classified substrate data derived from independent analyses of surface cover and particle size with cluster analysis, and gauged heuristic partitioning by measures of environmental parameters. Substrate classification using particle size exhibited superior sensitivity to differences in density and the proportion of samples that contained larvae. Gravel was rejected by age-1 larvae during all seasons and by age-0 and age-2 larvae in fall. Independent of season, there was greater use of silt by age-0 and age-1 larvae, while age-2 larvae utilised silt and sand equally.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".