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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".