Development of a Device to Pulping Fruits of Bocaiuva (Acrocomia aculeate sp.): Intended for the Communities that Practice Sustainable Agriculture or Strativism
Bibliographic record
Abstract
Bocaiuva (Acrocomia aculeata) fruit pulp has high potential for food, pharmaceutical and cosmetic applications and in the biofuel industry, however, the main hindrance for these applications is the availability of pulp in sufficient quantity. The pulp of this fruit is usually supplied by rural communities where the processors use manual pulping, which is not only time-consuming but yields product of low quality. Therefore, the objective of this work was to design, fabricate and evaluate the efficiency of an experimental bench device for pulping bocaiuva fruits. The bench device was designed to be compact and easy to operate, consisting of three units (processing, secondary, and peripheral) with intermittent operation and pulping performed by shearing. The performance evaluation of the machine was carried out with fruit dehydrated at 70 ºC and different times of dehydration (6, 12 and 24 hours). The pulp weight was evaluated every 15 seconds during the pulping process period (150 seconds). The highest pulping efficiency (96%) was obtained with fruits that were dehydrated for 24 hours, with a 120-second pulping, which resulted in a production rate of 5.5 kg h-1 This high efficiency makes this mechanized prototype effective for pulping bocaiuva fruits and, thus, it can replace manual pulping, especially in rural communities involved with the bocaiuva production chain.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".