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Record W4328017025 · doi:10.59194/mjee22241017b

Preliminary overview of body condition variation among dice snake populations from transboundary Lake Prespa

2022· article· en· W4328017025 on OpenAlexaff
Vukašin Bjelica, Dragan Arsovski, Marko Andjelković, Marko Maričić, Margareta Lakušić, Stefan Avramović, Ljiljana Tomović

Bibliographic record

VenueMacedonian Journal of Ecology and Environment · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicAmphibian and Reptile Biology
Canadian institutionsInstitute for Biological Sciences
FundersMinistarstvo Prosvete, Nauke i Tehnološkog RazvojaRufford Foundation
KeywordsDiceEcologyNatrixBiologyVariation (astronomy)MetapopulationAffect (linguistics)GeographyZoologyDemographyStatisticsPopulationPsychologyCommunication

Abstract

fetched live from OpenAlex

Studies of body condition in snakes are generally lacking and were only done on a few species and with limited sample sizes. Additionally, almost no studies considered how different factors affect body condition. We used a large dataset amassed over a 15 year-long ecological study to make a preliminary screening of body condition index (BCI) variation in a metapopulation of dice snakes (Natrix tessellata) in the region of Lake Prespa. We considered how factors such as sex, food, colour morph, locality and time affect BCI. We demonstrate a positive effect of food (relatively less in males), and lower BCI in females. Importantly, there is a strong seasonal effect, summer months having a positive effect as opposed to spring. The results of our study raise important considerations for future studies on snake BCI, but also conservation of freshwater ecosystems.

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 score0.040
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.231
Teacher spread0.215 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

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