Research on Vegetation Parameter Inversion of Open-Pit Mines Based on Lidar Data
Bibliographic record
Abstract
The monitoring of vegetation recovery was an important factor to evaluate the effectiveness of ecological restoration of open-pit mines. Lidar data can obtain the horizontal and vertical structures of trees that was wildly used to characterize the 3-dimensional (3D) structure of trees. In this paper, taking Ba’nan District of Chongqing as a study area, high density lidar data was applied to obtain the vegetation vertical structure parameters based on marker-controlled watershed segmentation method with IPTD filtering algorithm. The result showed, average tree height, diameter at breast height (DBH), crown diameter of open-pit mines was relatively lower compared with other areas outside as the trees of open-pit mines were mainly artificial planting and the growth time of trees was about 3-5 years. The trees had recovered well in the process of restoration. It indicated that the ecological restoration and planting of trees in open-pit mines were still in the process of recovery.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".