Response of Reeves’s Pheasants Distribution to Human Infrastructure in the Dabie Mountains over the Last 20 Years
Bibliographic record
Abstract
Human infrastructure development drives habitat loss and fragmentation worldwide. In China, over the last 20 years, rapid infrastructure development impacted the habitats of endangered species. To facilitate conservation efforts, studies of how human infrastructure affects the distribution of Reeves’s pheasant (Syrmaticus reevesii), an endangered species by the International Union for Conservation of Nature (IUCN) and a nationally protected species in China, are critically needed. We assessed how the distribution of Reeves’s pheasant was impacted by human infrastructure development over the past 20 years in the Dabie Mountains, the main distribution range of the species. We surveyed Reeves’s pheasants by direct sightings and indirect evidence through line transects which were randomly distributed in the Dabie Mountains from 2001 to 2002 and 2018 to 2019. We evaluated the variation of the roads and buildings in these areas in the last 20 years, and then modeled the relationship of the distribution of this pheasant with the road and building data from 2000 and 2017. Human infrastructure became more extensively distributed throughout the Dabie Mountains during the period, with all lands within 10 km of a road or a building. The distribution of Reeves’s pheasants became closer to the buildings and roads and there was a significantly positive relationship between the occurrence of Reeves’s pheasants and the distance to the nearest buildings and roads in 2018–2019. These results suggest that the increased density of buildings and roads in the Dabie Mountains may have caused negative effects on Reeves’s pheasants.
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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 | 0.000 | 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 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".