Bibliographic record
Abstract
The forest vegetation of Mt. Inwang was surveyed from August to October 2014. The vegetation of Mt. Inwang classified into 12 main communities according to the phisiognomic dominant species: P. densiflora commintity, P. densiflora-Prunus sargentii commintity, P. densiflora-Quercus mongolica commintity, P. densiflora-R. pseudoacacia commintity, Q. acutissima commintity, Q. acutissima- P. densiflora commintity, Q. acutissima-R. pseudoacacia commintity, Quercus variabilis commintity, Robinia pseudoacacia commintity, R. pseudoacacia-Pinus. densiflora commintity, R. pseudoacacia- Quercus acutissima commintity, R. pseudoacacia-Quercus serrata commintity. In addition, we identified the P. densiflora commintity distributed around the mountain top rocky ridge area, and R. pseudoacacia community is bordered on the inhabitant in the foot of mountain. P. densiflora-P. sargentii commintity showed higher species diversity than other communities. The degree of green naturality (DGN) 7 occupied 46.8% and DGN 6 occupied 40.6% of Mt. Inwang area. The coverage of herb layer of plant community was poor of 10%, shrub layer was 30~40%, and tree layer was 80% in all plant communities. However the coverage of sub-tree layer was very thin in pine tree communities. The frequency distribution in DBH-class of P. densiflora in the P. densiflora community showed scarce distribution of small diameter trees, and was opposed to the R. pseudoacacia that had large individuals of small diameter in the R. pseudoacacia community. The net primary productivity (NPP) was assumed as 1,504.42 g/m2/yr(use temp.) by Miami model, and 1,483.99 g/m2/yr by Montreal model, respectively.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".