Assessing the Effects of Free Fall Conditions on Damage to Corn Seeds: A Comprehensive Examination of Contributing Factors
Bibliographic record
Abstract
Corn is a staple food crop grown in over 100 countries worldwide. To meet the growing demand for corn, losses in its quality and quantity should be minimized. One of the potential threats to the quality and viability of corn is mechanical damage during harvesting and handling. Despite extensive research on corn, there is a lack of reliable data on the damage its seeds undergo when they are subjected to mechanical impact against different surfaces during handling and transportation. This study is designed to investigate the effects of (a) drop height (5, 10, and 15 m) during free fall, (b) impact surface (concrete, metal, and seed to seed), seed moisture content (10, 15, 20, and 25% w.b), and ambient temperature (−10 and 20 °C) on the percentage of physical damage (PPD) and physiological damage to corn seeds. The PPD and the extent of physiological damage were determined as the percentage of seed breakage and the percentage of loss in germination (PLG), respectively. The latter parameter was specifically chosen to evaluate seeds that showed no visible external damage, thus enabling the assessment of purely internal damage that PPD did not capture. This approach enabled a comprehensive analysis of free fall’s influence on the seeds’ quality and viability, providing a complete picture of the overall impact. Total damage was then calculated as the sum of PPD and PLG. An evaluation and modeling process was undertaken to assess how corn seed damage depends on variables such as drop height, moisture content, impact surfaces, and temperatures. The results revealed that seeds dropped onto metal surfaces incurred a higher total damage (15.52%) compared to concrete (12.86%) and seed-to-seed abrasion (6.29%). Greater total damage to seeds was observed at an ambient temperature of −10 °C (13.66%) than at 20 °C (9.46%). Increased drop height increased seeds’ mass flow velocity and correspondingly caused increases in both physical and physiological damage to seeds. On the other hand, increased moisture levels caused a decreasing trend in the physical damage but increased physiological damage to the seeds. The limitations of the developed models were thoroughly discussed, providing important insights for future studies. The results of this study promise to deliver substantial benefits to the seed/grain handling industry, especially in minimizing impact-induced damage.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".