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Record W2907553624 · doi:10.22621/cfn.v132i2.2035

Oviposition and subsequent depredation of Snapping Turtle (<i>Chelydra serpentina</i>) nests in fresh asphalt

2019· article· en· W2907553624 on OpenAlexafffundvenueabout
Shane R. de Solla, Jaclyn A. Gugelyk

Bibliographic record

VenueThe Canadian Field-Naturalist · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicTurtle Biology and Conservation
Canadian institutionsEnvironment and Climate Change Canada
FundersOntario Ministry of Natural Resources and ForestryMinistry of Natural Resources
KeywordsChelydraTurtle (robot)Nest (protein structural motif)PredationPredatorAsphaltZoologyBiologyFish <Actinopterygii>EcologyFisheryGeographyArchaeology

Abstract

fetched live from OpenAlex

On 28 June 2009, three Snapping Turtle (Chelydra serpentina) nests were found oviposited in fresh asphalt beside a bridge in Algonquin Provincial Park, Ontario. One nest was excavated immediately; the other two were left untouched. The two nests left in situ were revisited on 28 July when it was discovered that one nest had been depredated by an unknown predator. Evidence of the third nest was obliterated by tracks of large mammals crossing the bridge. These observations suggest that predators find turtle nests despite the strong odour of asphalt. To examine potential negative impacts of asphalt on turtle eggs, five clean Snapping Turtle eggs, collected elsewhere in Algonquin Provincial Park, were buried in asphalt on 28 July. After eight days, the translocated eggs had obvious staining and contained 0.081–0.376 μg/g (wet weight) polycyclic aromatic hydrocarbons (PAHs), presumably resulting from asphalt exposure. The risk of exposure to PAHs or other chemicals associatedwith asphalt is unknown, but the levels of PAHs in these eggs appeared lower than those associated with acute toxicity.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.058
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.009
GPT teacher head0.205
Teacher spread0.197 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2019
Admission routes4
Has abstractyes

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