Variation characteristics of the starting date and length of seasons over Northeast China during 1961 to 2010
Bibliographic record
Abstract
Using the recorded data from 90 meteorological stations over Northeast China during1961—2010 and the four-season division method proposed by Qianchen,the variation features of starting date and length of seasons were analyzed and its effects on agriculture were discussed. The results show that the average starting dates were April 10,June 25,August 11,October 20 and average length were80 d,51 d,72 d,171 d for spring,summer,autumn and winter respectively over Northeast China in the last 50 years with obvious spatial variations. It is clear that starting dates of spring and summer became earlier with the trends of- 1. 46 d /10 a and- 1. 99 d /10 a,while in autumn and winter,it became later with the values of 2. 05 d /10 a and 0. 90 d /10 a. The average length of spring,autumn and winter became shorter,amounting to- 0. 54 d /10 a,- 1. 15 d /10 a and- 2. 50 d /10 a respectively,the confidence levels in spring and autumn tended not to be able to pass,but it was at 0. 05 in winter. Summer became longer at the rate of 3. 38 d /10 a. The early coming of starting date of spring and its delay in autumn contribute to extending the growing period of crops and greatly affect the crop varieties and planting pattern.
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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 | 0.000 | 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".