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Record W6927355446 · doi:10.26107/rbz-2020-0015

On the Distribution, Behaviour and Seasonal Variation of Irrawaddy Dolphins (Orcaella brevirostris) in the Kep Archipelago, Cambodia

2020· article· en· W6927355446 on OpenAlexfundno aff

Bibliographic record

VenueInsight (University of Cumbria) · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
FundersInternational Conservation Fund of Canada
KeywordsArchipelagoForagingJuvenileAbundance (ecology)HabitatSeasonalityEndangered speciesMarine mammalCritically endangered

Abstract

fetched live from OpenAlex

Irrawaddy dolphins (Orcaella brevirostris) are a globally Endangered cetacean species found in rivers, lakes, estuaries, and coastal waters across Southeast Asia. Whilst much attention has concentrated on understanding freshwater populations of the species, marine populations have received less research attention, with the majority of marine studies focusing on determining abundance and distribution. As part of The Cambodian Marine Mammal Conservation Project, the current study utilises a combination of year-long land and boat survey techniques to identify seasonal critical habitats for the species in Cambodia's Kep Archipelago, as well as fill knowledge gaps on the species' behavioural ecology, to contribute to the design of effective and tailored regional conservation strategies. Results showed Irrawaddy dolphins to be present in the Kep Archipelago in all seasons, with the highest encounter rates in Summer Monsoon (May-September) and Post-Monsoon (October-November) seasons, and the lowest encounter rates in Pre-Monsoon season (March-April). Juveniles were present in all seasons, suggesting the region represents an important nursing ground for the population. Foraging was the most commonly observed behaviour, with significant associations found between certain behavioural states and events, group sizes and seasons, group sizes and juvenile presence, and swim styles and juvenile presence.

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 score0.117
Threshold uncertainty score0.722

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 head0.020
GPT teacher head0.192
Teacher spread0.171 · 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

Citations10
Published2020
Admission routes1
Has abstractyes

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