Survival and Spatial Ecology of the Snapping Turtle, <em>Chelydra serpentina</em>, on the Upper Mississippi River
Bibliographic record
Abstract
We studied the survival and spatial ecology of adult Snapping Turtles (Chelydra serpentina) on Pool 8 of the Upper Mississippi River (UMR) during 1997-2001. We captured 597 Snapping Turtles 745 times (333 adult males; 238 adult females; and 26 juveniles) at two study sites; Goose Island, Wisconsin and Lawrence Lake, Minnesota. From this sample, we radio-marked 104 Snapping Turtles of legal harvest size 128 times. Annual survival ranged from 0.857 to 1.000 and averaged 0.944 with Goose Island and Lawrence Lake estimates pooled. Legal harvest was the most important cause of mortality and accounted for 57% of documented deaths. Annual home range size using the Poly-Buff (PB) method averaged 11.13 ha and ranged from 2.20 ha to 37.18 ha. Emergent and rooted-floating aquatic vegetation were used disproportionally more than their availability and 72% of all locations collected during the active period occurred within these habitat types. Overall, radio-marked Snapping Turtles selected hibernacula in the following habitat categories; marshes (38%), main/side channels (28%), backwater sloughs and small ponds (14%), spring areas (10%), small tributary streams (7%), and tertiary channels (3%). Developing conservative, consistent harvest regulations among the states that border the UMR should be a management priority.
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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) | 0.001 | 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".