Measurement and prediction of the concentration of 1‐methylcyclopropene in treatment chambers containing different packaging materials
Bibliographic record
Abstract
Abstract BACKGROUND: 1‐methylcyclopropene (1‐MCP) has been shown to suppress ethylene response and extend the post‐harvest shelf life and quality of several fruits and vegetables. In the US and Canada, the label treatment dosage for apples is 1.0 and 0.6 µL L −1 , respectively. It has been demonstrated that wood and corrugated fiberboard materials, commonly found in apple storage facilities, absorb 1‐MCP. Losses of 1‐MCP during the exposure period might compromise the effectiveness of the product. The effects of type of material (corrugated fiberboard and high density polyethylene), relative humidity (50%, 80%, and > 95%), ratios of mass of packaging material (kg) per unit volume (m 3 ) of airspace in a treatment chamber, and initial concentration of 1‐MCP (10 and 20 µL L −1 ) on the available concentration of gaseous 1‐methylcyclopropene (1‐MCP) in an enclosed chamber were studied. RESULTS: The concentration of 1‐MCP declined in the presence of the materials tested, but the rate at which 1‐MCP gas was removed from the chamber headspace differed markedly. The average percentage loss for HDPE was between 10 and 12% at all conditions tested, while for corrugated fiberboard it ranged from 12 to 94%. CONCLUSIONS: The concentration of 1‐MCP at any time, t , follows an exponential decay behavior. For corrugated boxes, the rate at which 1‐MCP is removed increased up to 10‐fold as the relative humidity increased from 50 to 80%. The 1‐MCP depletion rate doubled as the ratio of material was increased from 4 to 8 kg of corrugated fiberboard m −3 air. An increase of initial concentration from 10 to 20 µL L −1 reduced the rate by half. This trend was also observed for HDPE boxes. Copyright © 2009 Society of Chemical Industry
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 | 0.001 | 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".