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Record W4224949872 · doi:10.1007/s43621-022-00083-w

Re-evaluating invasive species in degraded ecosystems: a case study of red-eared slider turtles as partial ecological analogs

2022· article· en· W4224949872 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueDiscover Sustainability · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicTurtle Biology and Conservation
Canadian institutionsYork University
FundersCollege of Engineering, Michigan State UniversityMichigan State University
KeywordsTurtle (robot)EcosystemWetlandEcologyInvasive speciesIntroduced speciesFreshwater ecosystemEnvironmental scienceBiologyGeography

Abstract

fetched live from OpenAlex

Abstract Exotic species are often vilified as “bad” without consideration of the potential they have for contributing to ecological functions in degraded ecosystems. The red-eared slider turtle (RES) has been disparaged as one of the worst invasive species. Based on this review, we suggest that RES contribute some ecosystem functions in urban wetlands comparable to those provided by the native turtles they sometimes dominate or replace. While we do not advocate for releases outside their native range, or into natural environments, in this review, we examine the case for the RES to be considered potentially beneficial in heavily human-altered and degraded ecosystems where native turtles struggle or fail to persist. After reviewing the ecosystem functions RESs are known to provide, we conclude that in many modified environments the RES is a partial ecological analog to native turtles and removing them may obviate the ecological benefits they provide. We also suggest research avenues to better understand the role of RESs in heavily modified wetlands.

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.

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)0.001
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 score0.219
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0000.001
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 head0.035
GPT teacher head0.302
Teacher spread0.267 · 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