Spatiotemporal Variation in Land Use of Northeast China Tiger and Leopard National Park
Bibliographic record
Abstract
This paper attempts to disclose the law of land use variation in the Northeast China Tiger and Leopard National Park (NCTLNP), and provide theoretical basis for eco-environment protection of the national park in future. The data on land use variation in four phases (i.e., 1995, 2005, 2015, and 2018) were selected for analysis based on the geographical information system (GIS). The variation and transfer features of land use were quantified, with the aid of single land use dynamic degree, comprehensive land use dynamic degree, and land use transfer matrix. The results show that: (1) In 1995-2008, the main land types in the NCTLNP were forest, grassland, and cultivated land, which took up more than 90% of the total area; the grassland area increased, while the areas of cultivated land and forest declined; forest was the land use with the largest transfer-out area (523.59 km2), about 55.29% of the total transfer-out area in the study area; (2) In the sample period, NCTLNP witnessed significant transfers between land uses; the transfers mostly occurred between forest, grassland, and cultivated land; forest transfers were observed in every county and city; the transfer of forest to grassland mainly concentrated in Dongning City. The research results lay the basis for building up a stereo eco-environment monitoring network in the study area, and provide the research direction for eco-environment protection in the NCTLNP.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".