Impactos das mudanças climáticas no zoneamento agroclimatológico do café arábica no Espírito Santo
Bibliographic record
Abstract
Objetivou-se com este trabalho definir, por meio do zoneamento agroclimatológico atual e para os próximos 100 anos, áreas com diferentes aptidões climáticas para a cultura do café arábica (Coffea arabica L.), no estado do Espírito Santo. Para isso, foram utilizados dados de temperatura média do ar e precipitação pluviométrica, em escala mensal e anual, de séries históricas representativas do período de 1976 a 2006. Foi necessário simular o efeito do incremento de temperatura de +1ºC, +2ºC, +3ºC, +4ºC e +5ºC, por meio da média obtida do resultado de seis modelos, a saber: GFDL-R30 (Geophysical Fluid Dynamics Laboratory, R-30 resolution model), CCSR/NIES (Center for Climate Research Studies Model), CSIROMk2 (Common wealth Scientific and Industrial Research Organization GCM mark 2), CGCM2 (Canadian Global Coupled Model version 2), ECHAM4 (European Centre Hamburg Model version 4) e HadCM3 (Hadley Centre Coupled Model version 3). Os resultados encontrados demonstraram que, atualmente, as áreas completamente aptas representam 19,49%, e com acréscimo de 5°C diminuirá para 0,02%, enquanto as áreas completamente inaptas passarão de 33,47% para 95,63% do território do Espírito Santo, tornando o café arábica impróprio para o cultivo no estado, se mantidas as características genéticas e fisiológicas que tem como limite de tolerância de temperaturas médias anuais entre 23°C e 24°C.
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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 | 0.000 | 0.001 |
| 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) | 0.003 | 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".