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Record W4408138432 · doi:10.1643/h2024045

Mass Mortality in a Community of Headstarted (Emydoidea blandingii) and Naturally Occurring (Chrysemys picta marginata) Freshwater Turtles in Protected Urban Wetlands

2025· article· en· W4408138432 on OpenAlexaffabout
Tharusha Wijewardena, Christine A. Drader, Donnell Marie-Leah Gasbarrini, Jacqueline D. Litzgus, Kevin C. R. Kerr, Nicholas E. Mandrak

Bibliographic record

VenueIchthyology & Herpetology · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicTurtle Biology and Conservation
Canadian institutionsToronto ZooLaurentian University
Fundersnot available
KeywordsPainted turtleWetlandEcologyGeographyBiologyTurtle (robot)

Abstract

fetched live from OpenAlex

Turtles experience numerous threats and high mortality in urban areas. Mass-mortality events (MMEs) are localized, sudden events resulting in a catastrophic increase in mortality rate. Turtles are susceptible to MMEs because their long generation times do not permit density-dependent compensation. We retrospectively investigated an MME in 2020 that affected two species, headstarted Blanding’s Turtles ( Emydoidea blandingii ) and naturally occurring Midland Painted Turtles ( Chrysemys picta marginata ) in Rouge National Urban Park (RNUP) in Ontario, Canada, where we monitored the freshwater turtle community from 2014 to 2022. In 2020, 48 juvenile headstarted Blanding’s Turtle and 57 Midland Painted Turtle carcasses were found, most (75%) of which were females. The turtles were likely depredated by Raccoons ( Procyon lotor ) or American Minks ( Neogale vison ). Documented mortalities were highest in release sites for headstarted turtles, possibly due to increased monitoring efforts at those sites. Conservation initiatives such as headstarting are useful to recover turtle populations, but MMEs may disrupt and delay population stabilization, especially in areas where other threats (e.g., subsidized predators, road mortality) are prevalent. We emphasize the importance of long-term monitoring of turtle populations, especially after conservation interventions, to detect challenges that affect their persistence.

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)0.001
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 score0.137
Threshold uncertainty score0.949

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.254
Teacher spread0.244 · 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

Citations3
Published2025
Admission routes2
Has abstractyes

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