Forecasting Brassica napus production under climate change with a mechanistic species distribution model
Bibliographic record
Abstract
Brassica napus, a versatile crop with significant socioeconomic importance, serves as a valuable source of nutrition for humans and animals while also being utilized in biodiesel production. The expansion potential of B. napus is profoundly influenced by climatic variations, yet there remains a scarcity of studies investigating the correlation between climatic factors and its distribution. This research employs CLIMEX to identify the current and future ecological niches of B. napus under the RCP 8.5 emission scenario, utilizing the Access 1.0 and CNRM-CM5 models for the time frame of 2040-2059. Additionally, a sensitivity analysis of parameters was conducted to determine the primary climatic factors affecting B. napus distribution and model responsiveness. The simulated outcomes demonstrate a satisfactory alignment with the known current distribution of B. napus, with 98% of occurrence records classified as having medium to high climatic suitability. However, the species displays high sensitivity to thermal parameters, thereby suggesting that temperature increases could trigger shifts in suitable and unsuitable areas for B. napus, impacting regions such as Canada, China, Brazil, and the United States.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".