A behavioural framework for trapping success and its application to invasive sea lamprey
Bibliographic record
Abstract
Scientific advances are needed to contend with invasive species. Trapping is often used for control or assessment, and understanding the invasive species’ behaviour is important for trapping success. A framework linking behaviour to trapping success and management responses was used to test hypotheses for the low trapping success of invasive sea lamprey (Petromyzon marinus) in the St. Marys River connecting Lakes Superior and Huron. Behaviour of sea lamprey approaching and at traps was quantified using external and internal tags and video. Multistate Markov models identified phenotypic and environmental factors influencing trapping success. Low trapping success for invasive sea lamprey in the St. Marys River is due to individuals not encountering traps, not entering upon encounter, not remaining at the trap, and not returning upon departure. All trapped lamprey were retained. Encounter with, and entrance into, traps varied with sea lamprey class, release date, and time of day, but not body length or river discharge. The conceptual and analytical methods applied here could be used to understand and improve trapping success for other invasive animals.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".