Forecasting analysis potential space distribution of Mikania micrantha in Guangzhou
Bibliographic record
Abstract
Mikania micrantha is one of the leading harmful and invasive forestry plants in Guangzhou.We selected 14 environmental factors and determined their quantitative relationship with the actual distribution of the specie in Guangzhou.By using principal component analysis,we determined main influencing factors.Based on the results,we estimated the potential distribution of Mikania micrantha.Results show that: six factors including elevation,mean annual temperature,annual mean sunshine duration,mean annual precipitation,wet quarter precipitation,dry quarter precipitation importantly affected the distribution of M.micrantha.The most suitable normal region was Huangpu,Luoguang;suitable normal region was Nansha,Tianhe and parts of districts of Zengcheng,Conghua and Panyu;unsuitable area was Liwan,Haizhu,small area of Panyu and Conghua.Above 96% area of Guangzhou was suitable for the growth of M.micrantha.And the potential distribution matched actual distribution.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".