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Record W1989685637 · doi:10.1017/s003060530500089x

Irrawaddy dolphin <i>Orcaella brevirostris</i> in the Cambodian Mekong River: an initial survey

2005· article· en· W1989685637 on OpenAlexaff
Ian G. Baird, Isabel Beasley

Bibliographic record

VenueOryx · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFisheryGeographyHabitatMekong riverBENGALPopulationDry seasonBayStructural basinWet seasonEcologyBiologyCartographyArchaeology

Abstract

fetched live from OpenAlex

Irrawaddy dolphins Orcaella brevirostiris are found in coastal waters from the Bay of Bengal east to Palawan, Philippines and south to northern Australia. They also occur in three large tropical river systems in South-east Asia: the Mekong, Mahakam and Ayeyarwady. In March and May 1997 approximately 350 km of riverine habitat in parts of north-east Cambodia were surveyed, discussions took place with local people, and reported dry season dolphin habitat was mapped. Our objectives were to investigate the status, habitat and distribution of dolphins in north-east Cambodia and identify threats to the continued survival of dolphins in the Mekong River Basin. Nine groups of dolphins were observed in the Mekong River. A ‘best’ estimate of 40 animals were seen. Irrawaddy dolphins were generally confined to sections of the river with water levels >8–10 m during the dry season. It appears that the Mekong River dolphin population is rapidly declining. In 1997 there were probably no more than 100¨C150 dolphins left in north-east Cambodia (including southern Laos) and no more than 200 within the entire Mekong River Basin, although these numbers remain tentative. Anthropogenic mortality is high, albeit largely unintentional, and there is considerable risk that the dolphin population will become locally extinct in the Mekong River in the near future. The establishment of community-managed deep water Fish Conservation Zones with government support may represent the best opportunity for reducing dry season dolphin mortality from large-meshed gillnet entanglement. Efforts to establish protected areas for dolphins are currently underway.

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 score0.097
Threshold uncertainty score0.193

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.036
GPT teacher head0.283
Teacher spread0.247 · 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

Citations44
Published2005
Admission routes1
Has abstractyes

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