Assessing The Extent Of Habitat Overlap And Resource Partitioning Between Ibex And Livestock In Khyber valley Pakistan
Bibliographic record
Abstract
Ibex and livestock distribution and habitat preferences were assessed using ecological niche factor analysis (ENFA). The study area was divided into 45 grid cells of 500 x 500 m pixels. Ibex and livestock were documented to occupy 30.61% and 26.5% of the study area, respectively, from June to November. Male and female ibex showed significant sexual segregation during this period. From November to May, male ibex occupied 32.6% of the study area and female ibex 34%. Livestock occupied 19% of the study area from June to September and 14.2% from September to May. The ENFA results indicated that both ibex and livestock show selective habitat preferences but are selecting for the same resources. The first two principal components (PCs) explained 49.3% and 17.88% of the variation in the data, respectively. The first PC was related to distance to settlements, distance to rivers, distance to roads, and rangelands. The second PC was related to rangelands and snow covers. The biplot of ENFA differentiated available habitats from habitat used (by ibex/livestock) through shades of grey, where light grey areas correspond to all the available habitat and dark grey area represented the used area (ecological niche of the animal). The ENFA plot indicated that though ibex and livestock have selective approach towards habitat usage, but selecting for the same resources
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".