A rapid assessment approach to prioritizing streams for control of Great Lakes sea lampreys (Petromyzon marinus): a case study in adaptive management
Bibliographic record
Abstract
We developed and evaluated an alternative method (rapid assessment or RA) for assessment of larval sea lampreys, Petromyzon marinus . We determined that using RA would result in at least as many, if not more, sea lampreys being killed than would using the current assessment method (quantitative assessment sampling or QAS) to select streams for lampricide treatment. Both assessment methods were carried out simultaneously throughout the entire Great Lakes basin from 2005 to 2007. RA required fewer resources than the current method and thus allowed for the chemical treatment of additional streams with lampricides, given a fixed overall budget for control. Population estimates generated from the QAS surveys showed that using RA would result in approximately equal numbers of metamorphosing lamprey and greater numbers of larval lamprey killed than by using QAS. Mark–recapture results indicated that prioritizing streams for treatment using RA may result in higher numbers of metamorphosing and larval lampreys killed than by using QAS. RA is currently being adopted throughout the Great Lakes as the method for assessing larval sea lamprey populations. Other fishery management programs may benefit from examining the value of their assessment programs relative to other uses of resources.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".