The changes of plant communities and the landscape heterogeneity in Shule River Basin
Bibliographic record
Abstract
The vegetation communities and landscape types in Shule River basin were classified and refined based on 231plant-square survey combined with remote sensing data and existing vegetation data to reveal the characteristics of spatial distribution and other changes of vegetation types with the terrain and other elements.The vegetation landscape heterogeneity was analyzed using the grid line relative frequency from linear sampling method.The results showed that Alhagisparsifolia,Nitraria tangutorum,Lycium ruthenicum,Tamarixsp.,Kalidium foliatum,Sympegma regelii were the main vegetation types in the study area which occupied an absolutely dominant position.These vegetation characteristics of staggered distribution and mutual influence were the important factors influencing the structure,function and dynamics of ecosystems to form unique vegetation communities and landscape system of Shule River.Along with different altitude,the vegetation types were richer and the distribution was more complex with the relative frequency less than 50%.For the different gradient performance of landscape heterogeneity,therelative frequency of dominant populations in different vegetation types were not high and vegetation communities and landscape heterogeneity were lower.Along the vegetation of latitudinal direction,horizontal cross-sectional distribution was single with a large area which had domain population advantages.
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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".