Mango Fruit Detachment of Trees after Applying a Blend Composed of HNO3 and Charcoal Activated
Bibliographic record
Abstract
As young workers prefer urban labors and migrate to USA and Canada, mango harvesting is becoming scarce on Mexican coasts. This seasonal labor is becoming expensive and when many orchards produce fruit simultaneously, grower losses increase. In this research, an innovative fruit detachment method was tested after applying a viscous paste to the pedicel of mango fruits hanging in the tree. Activated carbon or charcoal (AC), was mixed with different amounts of nitric acid to provide three AC composite blends named: light, medium, and dense. The nanomaterial was applied with a brush to the fruit pedicel/peduncle taking up to 4 h before the mango fruits felt to a net below the tree canopy. Mango detachment experiments indicated that the medium blend was the most efficient in releasing the fruit, taking an average of 2 h. The dense nano-material decreased latex exudation to 7% of the fruits. Fruit maturity emerged as a crucial factor for detachment time, followed by mango weight.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".