Potential distribution and habitat suitability of <i>Picea crassifolia</i> with climate change scenarios
Bibliographic record
Abstract
The Qinghai–Tibet Plateau in China is a region strongly impacted by climate change, yet its effects on the keystone endemic forest species Picea crassifolia Kom. are unknown. Understanding the changes in potential distribution and habitat suitability of P. crassifolia forest with climate change will contribute to water conservation, forest management, and ecological protection in the upper reaches of the Yellow River. A total of 129 records of species distribution data and 19 environmental variables were chosen for modeling. The MaxEnt (maximum entropy) model was used to analyze the main environmental factors affecting the potential distribution of P. crassifolia in two periods (2050s and 2070s) and four representative emission pathways (RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5). The main results are follows: (i) the most important environmental variables affecting distribution of P. crassifolia and percentage variance explained were altitude (41.85%), precipitation of driest month (19.76%), slope (12.35%), annual precipitation (6.56%), precipitation of wettest month (5.73%), and precipitation of warmest quarter (5.12%); (ii) habitat suitability of P. crassifolia shifted to the northwest and into high-altitude areas under climate change scenarios, but its core distribution areas were concentrated in northeastern Qinghai–Tibet Plateau, Qilian Mountains, southern Ningxia, and Helan Mountains; and (iii) the total area of potential suitable habitat of P. crassifolia will change significantly in the future, and change of habitat area of not suitable, low, moderate, and high suitability exceed 60%.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".