Quantitative Assessment on Spatial Suitability for Tobacco Planting in Bozhou in Northern Anhui Province, China
Bibliographic record
Abstract
While Qiaocheng district currently is the main tobacco planting region in Northern Anhui Province, China, the quantitative assessment on spatial suitability for tobacco planting is necessary for the scientific instruction in the adjustment of planting regionalization and fertilization. In this paper, soil samples of the plough layers were collected from 1224 typical farmlands in 2008, and 18 types of soil properties, pH, clay, silt, sand, organic matter, total nitrogen, available phosphorus, slowly available potassium, rapidly available potassium, exchangeable Ca2+ and Mg2+, Cl-, HCO3-, SO42-, available Fe, Mn, Cu and Zn were measured. Meanwhile irrigation water samples of 90 typical pumping wells were collected, and Cl-, HCO3-, SO42-, K+, Ca2+ and Mg2+ were measured. The above measured properties were used as the assessing indexes for spatial suitability for tobacco planting, in which the weights were generated by the method of principal component analysis and the fuzzy membership functions were produced based on the practical experiences with the related literatures. The soil suitabilities were quantitatively assessed using the ArcGIS 10.0 platform. The experimental results showed that: (1) areas of the highest, higher, middle, lower and lowest levels of suitability are 4.28×104, 4.36×104, 4.82×104, 4.47×104 and 4.81×104 hm2. respectively, constituted of 18.84%, 19.16%, 21.20%, 19.66% and 21.14% of the total area of the farmland, respectively; (2) in general, the Northeast, Northwest, Southeast and Southwest regions are the most suitable areas, and (3) for tobacco farmland in the North region, more potassium fertilizers should be applied due to the low available potassium content in soil, and the flood irrigation should be prevented due to the high content of Cl- in irrigation water. Chengfu in the Sourtheast, Shihe and Feihe in the Southwest, and Longyang and Gucheng in the South could be regarded as the new potential tobacco planting regions.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".