Ecological security fuzzy evaluation for Tianzhu alpine rangeland based on GIS
Bibliographic record
Abstract
Evaluation of rangeland ecological security is the assessment of ecological security levels,according to rangeland natural environment pressure and the needs of human in special stage of social development.Index systems and methods of evaluation are the important study contents in the evaluation of rangeland ecological security.With direction of driving force-state-response(DSR),we built the multilevel index systems which are composed of 21 specific indexes,and evaluated the rangeland ecological security of Tianzhu alpine rangeland with fuzzy evaluation model.Supported by GIS software,used map data,remote sensing data,observational data and statistical data of Tianzhu alpine rangeland,applied with information mapping method,we generated condition maps of water-heat and land,evaluation index maps according to classification unit of township index and security evaluation result maps based on fuzzy evaluation model of Tianzhu alpine rangeland.The results indicated that Tianzhu alpine rangeland ecological security condition is in relatively safe state as a whole,but the area in relatively unsafe and unsafe state approach 1/4 of total area.The problems of rangeland ecological security should be paid more attention.Rangeland ecological security condition in the study area showed obvious spatial variation.The study area can be divided into 3 areas: northwestern rangeland ecological security protect region,eastern rangeland ecological relative security protect and control region,midland rangeland ecological security control region.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".