Reconsidering residency: characterization and conservation implications of complex migratory patterns of shortnose sturgeon (<i>Acispenser brevirostrum</i>)
Bibliographic record
Abstract
Efforts to conserve endangered species usually involve attempts to define and manage threats at the appropriate scale of population processes. In some species that scale is localized; in others, dispersal and migration link demic units within larger metapopulations. Current conservation strategies for endangered shortnose sturgeon (Acipenser brevirostrum) assume the species is river resident, with little to no movement between rivers. However we have found that shortnose sturgeon travel more than 130 km through coastal waters between the largest rivers in Maine. Indeed, acoustic telemetry shows that shortnose sturgeon enter six out of the seven acoustically monitored rivers we have monitored, with over 70% of tagged individuals undertaking coastal migrations between river systems. Four migration patterns were identified for shortnose sturgeon inhabiting the Penobscot River, Maine: river resident (28%), spring coastal emigrant (24%), fall coastal emigrant (33%), and summer coastal emigrant (15%). No shortnose sturgeon classified as maturing female exhibited a resident pattern, indicating differential migration. Traditional river-specific assessment and management of shortnose sturgeon could be better characterized using a broader metapopulation scale, at least in the Gulf of Maine, that accounts for diverse migratory strategies and the importance of migratory corridors as critical habitat.
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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 | 0.001 | 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) | 0.001 | 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".