Forecasting the response of Great Lakes sea lamprey (<i>Petromyzon marinus</i>) to barrier removals
Bibliographic record
Abstract
A key uncertainty surrounding barrier removals in the Great Lakes is the response of invasive sea lamprey (Petromyzon marinus) populations to realized increases in available habitat for adfluvial species. We addressed this uncertainty by applying a management strategy evaluation model, originally developed to inform sea lamprey management in the Great Lakes, to forecast the effects of barrier removal on Lake Michigan sea lamprey abundances. We used this model to characterize the response to systematically increasing habitat availability and a specific proposed barrier removal. Our results suggest the removals allow novel production from newly opened habitat and, assuming a fixed budget for sea lamprey control, decrease the overall effectiveness of control, leading to disproportionate increases in abundance. The case study demonstrated that evaluating population effects only at the scale of watersheds directly affected by barrier removals would substantially underestimate effects at the scale of Lake Michigan. Similar population responses are possible when evaluating the effects on desired species. Our findings highlight the importance of considering trade-offs for barrier removals and selecting the appropriate scale for forecasting.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".