Sequence of Workable Days for Mechanized Harvest of Sugarcane in Southern Brazil
Bibliographic record
Abstract
The probabilities of workable days (WD), as well as probability of having a given sequence of days for sugarcane mechanized harvest in Southern Brazil is a very useful information for planning of such operation. Thus, the aim of this study was to determine the simple and conditional probabilities of WD for the abovementioned field operation in the State of São Paulo, Brazil, by means of the Markov Chain, to define the probabilities of sequences of WD. The number of WD (NWD) was determined for 32 years for ten sites using as criteria soil water holding capacity of 40 mm, rainfall ≤ 3 mm and relative soil water storage ≤ 90%. Based on NWD dataset, the simple probabilities of WD and non-workable (NW) days, as well as the conditional probabilities were determined. Finally, the probability of sequences of WD per ten-day period was obtained by the Markov chain. The results showed that Western, Northwestern and Northern, on average, were more likely to have WD compared to Southern and Eastern regions of the state. In addition, the most likely periods of WD were between April and September, being the first ten-day period of July the one with the highest possible probability (≥ 90%). The probability of having a workable day given that the previous day was workable always remained at a minimum of roughly 50% along with a maximum close to 90% at all assessed sites. Finally, the probability of a sequence of eight or more WD per ten-day period was always below 40% along the year, showing that is difficult to have such a long period available for planning sugarcane mechanized harvest in the assessed locations. Therefore, we recommend that fleets sizing should be defined as a function of NWD in conjunction with the probability of the sequence of WD at a given ten-day period.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".