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Record W7161967761 · doi:10.82308/40581

Habitat selection, movement patterns, and demography of common musk turtles (Sternotherus odoratus) in southwestern Québec

2008· dissertation· en· W7161967761 on OpenAlexaboutno aff
Pascale. Belleau

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicTurtle Biology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsHabitatBeaverHome rangeCarapaceRange (aeronautics)PopulationSex ratioVegetation (pathology)

Abstract

fetched live from OpenAlex

I studied the common musk turtle (Sternotherus odoratus) at the northern limit of its range at Norway Bay, Quebec, from April to October 2006. Common musk turtles are habitat specialists and are selective of their habitats at the study-area and home-range scales. Beaver ( Castor canadensis) lodges were preferred at the study-area scale. Common musk turtles also preferred beaver lodges, emergent wetlands, aquatic beds with floating and submerged vegetation as well as rocky shores at the home-range scale. At the location scale, common musk turtles chose shallower and cooler sites that contained more logs and submerged vegetation than the sites available at random. There was no significant effect of sex on habitat use at the location scale. There was no significant difference in mean daily movements between the sexes during the active season. However, sex and month probably interact together to influence the mean distance traveled daily by common musk turtles in Norway Bay. Males appeared to move more than females in May, July, and October. Females appeared to move more daily than males in August and September. Neither sex appeared to move more daily in June. However, our small sample size did not allow us to conduct a conclusive analysis. The mean home-range area was 23.9 ha and was not different between sexes. I estimated a density of 4.1 turtles/ha and a sex ratio of 1.7M: 1F. The population includes 59.6% males, 35.8% females, and 4.6% juveniles. Adults ranged from 77 mm to 133 mm in carapace length.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.729
Threshold uncertainty score0.924

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.006
GPT teacher head0.206
Teacher spread0.200 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2008
Admission routes1
Has abstractyes

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