Space use of juvenile and subadult yellow perch (<i>Perca flavescens</i>) in the Detroit River using acoustic telemetry: incorporating variable detection ranges in vegetated areas
Bibliographic record
Abstract
Understanding the space use of fishes in early life stages provides information that can contribute to effective fisheries management; however, it can be difficult to track fish in shallow, densely vegetated areas. Using acoustic telemetry, 60 subadult yellow perch (Perca flavescens) were tagged and monitored in a vegetated area of the Detroit River (May–November 2018). Variable detection range from submerged aquatic vegetation (SAV) was incorporated in estimates by applying a spatiotemporal correction to aid with interpretation of seasonal changes in activity space. Although subadult yellow perch were commonly detected in the array, demonstrating the importance of SAV habitat (mean detection residency index: 0.85), 60% of individuals were not detected following August, the period with highest activity space estimates, likely due to seasonal movements and predation. Individuals were more commonly detected during the daylight hours compared to night, but activity peaked at crepuscular periods. This study provided spatial information about the often-overlooked early life history of yellow perch, increasing the ecological information available for a species of management and conservation interest in the Great Lakes.
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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".