Understanding Bison Carrying Capacity Estimation in Northern Great Plains Using Remote Sensing and GIS
Bibliographic record
Abstract
As an iconic species linked to First Nations culture and economy in western North America, wild plains bison (Bison bison bison) currently rely on intensive management to persist. Yet, their presence maintains grassland ecological function, protects biodiversity and preserves important cultural heritage. Estimating carrying capacity is a key to achieve wildlife conservation without risking overall ecosystem health. Plains bison carrying capacity should be estimated to provide guideline for developing subsequent management plans. We reckoned that drivers of plains bison carrying capacity are forage availability and animal requirement, meanwhile its adjusting factors comprise spatio-temporal distribution and sustainable consideration. An integration of remote sensing and GIS can help to investigate variables of carrying capacity. Also Habitat Suitability Model built in Geographic Information System (GIS) is able to compile variables influencing carrying capacity. The review found multiple challenges of carrying capacity estimation in terms of implementing and practicing not only from remote sensing and GIS perspective. We expect that the advancement of remote sensors in accordance with modern GIS technology can provide timely effective carrying capacity estimation to achieve conservation goals of animal species as well as maintain sustainable ecosystems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".