Geographic Information System (GIS) Application for Camels: The Case of Al Ain, United Arab Emirates (UAE)
Bibliographic record
Abstract
Since the 1980s the United Arab Emirates (UAE) has witnessed rapid socio-economic transformation, which has affected many aspects of land and life that are of interest to geographers, including the spatial distribution of camels. This study utilized Geographic Information System (GIS) and spatial analysis to understand the changing distribution of camels. The research revealed that camels in the UAE are now clustered near racetracks and not near traditional magnets such as sources of water and grazing areas. Such clustering is unique to the UAE and is unknown in the other countries of the Middle East and North Africa. The clustering is due to social and economic factors related to racing. The chief social factor is the high prestige associated with winning a camel race, and the main economic factor is the high value of winnings from camel racing (prizes may reach US$ 3 million). The results of this study have helped to change peoples' perceptions about the capabilities and potential use of GIS for issues ...
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".