Number and Nest-Site Selection of Breeding Black-Necked Cranes Over the Past 40 Years in the Longbao Wetland Nature Reserve, Qinghai, China
Bibliographic record
Abstract
Abstract Black-necked crane (Grus nigricollis, BNC) is an endangered species classified as vulnerable under the revised IUCN Red List, and it faces serious threats from human activities and habitat variations. We investigated and analyzed the population and nesting microhabitat of BNCs in the Longbao National Nature Reserve (NNR) from 1978 to 2016. The number average growth rate of each decade was different, and we analyzed the reasons for the number increase from several aspects, including land cover and climate change. The establishment of the Longbao NNR represented an effective method of protecting endangered animal species. However, the land cover classification results of Landsat images showed that the marsh wetland, which is the BNC’s primary habitat, decreased, while artificial buildings, including roads and houses, increased, which affected the habitat of BNCs. The average temperature increase over the past 40 years has also had an impact on the number of BNCs. We observed 9 BNC nests, which included 5 island nests and 4 grass nests, by telescope in 2018. BNCs prefer to nest in swampy wetlands or on islands with open water or star-like distributions. The results of the principal component analysis showed that among the six microhabitat factors (elevation, habitat type, distance to roads, shortest distances to nearest water body, nearest distance between nests and disturbance), the nearest distance between nests and habitat type were the primary factors influencing nesting site selection. To promote this species, we suggest decreasing wetland fragmentation, reducing habitat degradation and providing an undisturbed habitat.
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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 | 0.000 | 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".