Seasonal Effects on Starch Contents Evaluated in Cassava Roots
Bibliographic record
Abstract
Starches have a wide range of uses and their consumption has increased over the years, resulting in a growth in the agro-industries that produce them. Cassava is a very important plant for agri-business and one of the main products obtained from its roots is starch. Although cassava can be harvested throughout the year, its quality varies greatly through the seasons; this is because it is influenced by soil and climatic factors, as well as the genetic characteristics of the species. These influences result in seasonal oscillations in root classification based on the starch content available at the time of product delivery. Faced with this problem, the objective of this study was the collection and evaluation of documentary data for 3 years of product quality samples. This was done in order to observe the situation and propose tools that can minimize problems resulting from the quality of raw material received by starch producers throughout the year. It was observed that in the winter period there was an increase in root starch content, despite the differences between the months not being statistically significantly, they are financially representative of this agro-industry sector. At the end of the study, a proposal for a methodology for calculating payment per gram of starch is presented in order to minimize the problem.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".