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Record W3181187334 · doi:10.6000/1927-520x.2021.10.08

Conservation Efforts of Asian Wild Water Buffalo

2021· article· en· W3181187334 on OpenAlexvenueno aff
Pramod Kumar Kherwar, Ajaya Bhattarai

Bibliographic record

VenueJournal of Buffalo Science · 2021
Typearticle
Languageen
FieldEngineering
TopicCavitation Phenomena in Pumps
Canadian institutionsnot available
Fundersnot available
KeywordsIUCN Red ListThreatened speciesNational parkWildlifeBubalusWetlandFloodplainBiodiversityNature reserveGeographyNear-threatened speciesWildlife conservationAgroforestryEnvironmental protectionFisheryEcologyBiologyHabitat

Abstract

fetched live from OpenAlex

Asian Wild Water Buffalo with the scientific name Bubalus bubalis arnee is recorded as an imperiled species in the International Union for Conservation of Nature (IUCN's) Red List of Threatened Species wetland-subordinate. More number Wild Water Buffalo in Nepal are found at the Koshi Tappu Wildlife Reserve (KTWR), situated on the floodplain of the Koshi River in Province 1. This species is in danger because of a high anthropogenic pressing factor going from natural surroundings weakening to hybridization with homegrown Buffalo. Various preservation and work mediations have been attempted to shield the biodiversity, especially the wild buffalo populace in the KTWR. The people group-based supportable administration approach profiting both protection and work of neighborhood individuals is important to guarantee the drawn-out preservation of the species. But it is not happening, so the Government of Nepal translocated 18 Wild Water Buffaloes to Chitwan National Park, and the environment is not suitable for them. There is the possibility to translocate Wild Water Buffaloes in the Babai flood plain of Bardia National Park for conservation.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.010
GPT teacher head0.224
Teacher spread0.214 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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