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Record W1750575411 · doi:10.22621/cfn.v123i4.1002

Survival and Spatial Ecology of the Snapping Turtle, <em>Chelydra serpentina</em>, on the Upper Mississippi River

2009· article· en· W1750575411 on OpenAlexvenueno aff
R. Neal Paisley, John F. Wetzel, J. Stuart Nelson, Cindy Stetzer, Mark G. Hamernick, Benjamin P. Anderson

Bibliographic record

VenueThe Canadian Field-Naturalist · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicTurtle Biology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsChelydraTributaryHabitatMarshTurtle (robot)EcologyRange (aeronautics)GeographyGooseWetlandSTREAMSHome rangeSan JoaquinBiologyFisheryEnvironmental science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.572
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.011
GPT teacher head0.205
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2009
Admission routes1
Has abstractyes

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