The landscape function regionalization for the Shiyang River Basin based on GIS and RS
Bibliographic record
Abstract
Ecological function regionalization is the foundation of the rational management and sustainable utilization of ecosystems and nature resources.The regional difference of a basin ecosystem's components and its influence factors decide the different landscape function,which have different eco-functions in a system in dryland area.Regionalize scientifically to the landscape function can provide the scientific basis for regenerating and conserving ecological environment.A new theory of ecological function regionalization is proposed based on landscape ecology,which makes up the shortages of ecological countermeasures that just consider geomorphic unit and ecological elements,but ignore the ecological energy cycle and optimize landscape units range from angle of landscape ecology.Based on analyzing the basic features of the ecological environment and landscape functional costs as well as optimized units in the Shiyang River Basin,we discussed the principles,bases,methodology and nomenclature of landscape function regionalization as well as the application of GIS and RS in landscape function regionalization.Based on the ecosystem assessment,three landscape functional regions,and 9 eco-funct ional zones were subdivided by the method of cost resistance.Besides,the basic futures of landscape function regions and optimized measures were also analyzed.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".