Spatiotemporal Evolution and Features of Net Carbon Sink of Farmland Vegetation in Chongqing, China
Bibliographic record
Abstract
To promote sustainable development of agriculture, it is critical to reduce carbon sources and increase carbon sinks in farmland ecosystem by rationalizing the measures of agricultural management.This calls for scientific evaluation of net carbon sink (NCS) and its spatiotemporal evolution of farmland vegetation in a region.Taking 38 districts/counties of Chongqing, China as objects, this paper estimates the farmland vegetation NCS of Chongqing, based on statistics of crop yields and farmland inputs in 2000-2017.Then, geographical techniques were employed to analyze the features, regional difference and spatial evolution of NCS in Chongqing and its districts/counties.The main results are as follows: (1) From 2000 to 2017, the NCS and NCS strength (NCSS) of farmland vegetation in Chongqing both increased with fluctuations.The carbon sink, carbon emissions and carbon absorption increased across the board.The evolution of farmland vegetation can be divided into a wavy decline phase from 2000 to 2006, and a gradual increase phase from 2006 to 2017.(2) The source/sink structure of farmland vegetation in Chongqing remained stable in 2000-2017.The main sources are pesticide and tillage, and the main sinks are corn, rice, vegetables and oil crops.(3) In term of space, the farmland vegetation NCS and its center of gravity concentrated in the west zone and northeast zone.In general, the farmland vegetation of Chongqing boasts a strong carbon sink function; the west zone and northeast zone have the highest farmland vegetation NCSs; the west zone is the demonstration region of NCSS improvement.
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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 | 0.002 |
| 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) | 0.001 | 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".