A Bayesian multistate approach to evaluate movements of an invasive freshwater estuarine-opportunist
Bibliographic record
Abstract
Coastal rivers and estuaries provide habitat and migratory corridors for freshwater estuarine-opportunists. We evaluated movement patterns of 61 blue catfish (Ictalurus furcatus) in the tidal York and Rappahannock rivers in Virginia, USA with acoustic telemetry from July 2015 to June 2016. To evaluate river-specific movements, we utilized a multistate Cormack–Jolly–Seber (CJS) model within a Bayesian framework to estimate probabilities of detection and transition (movement) among established salinity zones (i.e., tidal-fresh (0‰–0.5‰), oligohaline (>0.5‰–5‰), mesohaline (>5‰–18‰)). We considered flow as an environmental covariate. Despite high site fidelity in tidal-fresh zones, some individuals displayed movements into oligohaline and mesohaline habitats indicative of partial migration. Once downstream movement occurred, the probability of staying in the new salinity zone was higher than the probability of movements to other salinity zones. In the Rappahannock River only, movement upstream from mesohaline habitats was associated with below average flow. As flow increased, the probability of remaining in oligohaline and mesohaline zones increased. Our study shows blue catfish can move into downstream areas of tidal rivers with elevated salinities and that increased freshwater flow may allow them to remain in these habitats for extended durations.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".