MétaCan
Menu
Back to cohort
Record W2921058817 · doi:10.1080/11956860.2019.1587862

Impact of agrarian land use and land cover practices on survival and conservation of nilgai antelope (<i>Boselaphus tragocamelus</i>) in and around the Abohar wildlife sanctuary, northwestern India

2019· article· en· W2921058817 on OpenAlexvenueno aff
Parteek Bajwa, N. P. S. Chauhan

Bibliographic record

VenueEcoscience · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyFencingAgrarian societyWildlifeLand coverBiodiversityHabitatEcologyDistribution (mathematics)Land useAgricultureAgroforestryPhysical geographyEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

There is an information gap on biodiversity effects of land use and land cover (LULC) dynamics of agrarian landscapes. Such data are essential for policy making and species management in agro-ecosystems. Thus, changes in LULC inside the Abohar wildlife sanctuary and its eco-sensitive zone were investigated using LISS-III satellite images from 2003 to 2016. The area under cropland witnessed a 3.6% increase, whereas wastelands significantly declined by 4.3%. Further, the impact of LULC dynamics on nilgai (Boselaphus tragocamelus) was determined through mortality distribution in the sanctuary from 2012 to 2017. The spatio-temporal distribution pattern of mortality revealed that a total of 336 nilgai died during the six years studied. Free-ranging feral dogs, in conjunction with fencing and road accidents, were the major factors involved in nilgai casualty. Fatalities were clustered in regions with significant LULC change. The results confirmed that intensified development and reuse of derelict agricultural fields disturbed nilgai ecology and habitat use pattern. Human-wildlife conflicts in agrarian landscapes are an increasing concern and should be managed following identification of sensitive areas.

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)0.001
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 score0.001
Threshold uncertainty score0.746

Codex and Gemma teacher scores by category

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

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 head0.017
GPT teacher head0.246
Teacher spread0.229 · 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

Citations7
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueEcoscienceSame topicWildlife Ecology and ConservationFrench-language works237,207