Studies on the dynamics of patch size and grain structure of landscape elements in the forest restoration process
Bibliographic record
Abstract
Supported by ARC/INFO,using four stages of Ariel image since 1959 as basic data source,this paper analyzed and revealed the dynamic rules of landscape elements in forest landscape restoration of Guandishan Mountains Forest District in the Central West of Shanxi,China Patch Mean Size and Grain Size Structure of landscape element were used as basic indices to reveal the dynamic characteristic features of forest patches in forest restoration in study area Some basic rules and process of patch restoration and succession were demonstracted in the complex landscape changes co controlled by vegetation succession and disturbance regime The forest landscape in the study area is still being of final grain landscape since 1959 But the mean sizes and grain structures of different landscape elements were changed obviously with different change patten in the three stages of forest restoration process These differences reflected the ecological dynamic features of these different types of forest patch in landscape scale succession process
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".