Evaluation of Marginal Land Potential and Analysis of Environmental Variables of Jerusalem Artichoke in Shaanxi Province, China
Bibliographic record
Abstract
Based on the maximum entropy modeling (Maxent) and ArcGIS tool, this study assessed the potential of marginal land and analyzed the impact of environmental variables for Jerusalem artichoke (Helianthus tuberosus L.) in Shaanxi Province, China. The results showed that the dominant land type used for the growth of Jerusalem artichoke was moderately dense grassland. Additionally, significant environmental variables of Jerusalem artichoke and their suitable range in Shaanxi Province were average slope (SLP, 0–5°C), average soil depth (DPT, 1.50–1.60 m), max temperature of the warmest month (Bio5, 30–31°C), annual mean temperature (Bio1, 16.5–18.0°C), precipitation of the wettest quarter (Bio16, 0.01–0.02 m), July solar radiation (SR7, 1.66–1.67 × 107 W/m2), precipitation seasonality (Bio15, 50–60%), precipitation of the driest quarter (Bio17, 0–0.005 m), and isothermality (Bio3, 265–275). Furthermore, the suitable area was mainly distributed in southern (mainly Hanzhong, Ankang, and Shangluo) and northern (mainly Yan’an and Yulin) parts of Shaanxi Province, covering around 8.81 × 1010 m2 and accounting for 42.8% of the total area of the Shaanxi Province. This study can provide a reference for the rational planting of Jerusalem artichoke in Shaanxi Province.
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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 | 0.002 | 0.002 |
| 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 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".