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Waterhole use and diel activity pattern of ocelots in Calakmul rainforest, Mexico

2022· article· en· W4304091965 on OpenAlexfundno aff
Elisa Sandoval‐Serés, Khiavett Sánchez-Pinzón, Rafael Reyna‐Hurtado

Bibliographic record

VenueRevista Mexicana de Biodiversidad · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsnot available
FundersComisión Nacional de Áreas Naturales ProtegidasUniversity of OxfordConsejo Nacional de Ciencia y TecnologíaMcGill University
KeywordsNocturnalDiel vertical migrationCrepuscularEcologyGeographyRainforestBiology

Abstract

fetched live from OpenAlex

We aimed to evaluate the temporal and spatial use of waterholes by ocelots in Calakmul Biosphere Reserve (CBR), Mexico. From 2014 to 2017, we monitored 11 waterholes with camera traps. We compared diel activity patterns with circular statistics depending on waterholes’ level of human intensity and distance to the Calakmul road, seasonality, and sex. We identified 40 different ocelots. Four waterholes were the most important ones, being 2 of them close to the road. Individuals took on average 19 days to return to waterholes. The diel activity of ocelots was 63.67% nocturnal, 20.70% crepuscular and 15.60% diurnal, and they were more diurnal in waterholes distant from the road. Their activity pattern was bimodal and it did not change between any of the categories tested. This is the first study to determine the spatial and temporal activity of ocelots in waterholes of Mexico. Ocelots are mainly nocturnal, and this pattern is conserved throughout CBR, however, they are able to adjust slightly their activity depending on extrinsic factors, such as an increased human presence. In the Calakmul region, all waterholes are crucial, and we particularly emphasize the conservation of the most important waterholes for ocelots, especially the ones close to the road.

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 score0.049
Threshold uncertainty score0.954

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)0.0010.000

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 head0.022
GPT teacher head0.229
Teacher spread0.207 · 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
Published2022
Admission routes1
Has abstractyes

Explore more

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