How does salinity influence habitat selection and growth in juvenile American eels<i>Anguilla rostrata</i>?
Bibliographic record
Abstract
The influence of salinity on habitat selection and growth in juvenile American eels Anguilla rostrata captured in four rivers across eastern Canada was assessed in controlled experiments in 2011 and 2012. Glass eels were first categorized according to their salinity preferences towards fresh (FW), salt (SW) or brackish water (BW) and the growth rate of each group of elvers was subsequently monitored in controlled FW and BW environments for 7 months. Most glass eels (78-89%) did not make a choice, i.e. they remained in BW. Salinity preferences were not influenced by body condition, although a possible role of pigmentation could not be ruled out. Glass eels that did make a choice displayed a similar preference for FW (60-75%) regardless of their geographic origin but glass eels from the St Lawrence Estuary displayed a significantly higher locomotor activity than those from other regions. Neither the salinity preferences showed by glass eels in the first experiment nor the rearing salinities appeared to have much influence on growth during the experiments. Elvers from Nova Scotia, however, reached a significantly higher mass than those from the St Lawrence Estuary thus supporting the hypothesis of genetically (or epigenetically) based differences for growth between A. rostrata from different origins. These results provide important ecological knowledge for the sustained exploitation and conservation of this threatened species.
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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 | 0.000 |
| 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) | 0.000 | 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".