Assessing the effect of machine automation on operator heart and breathing rate during mechanical harvesting of wild blueberries
Bibliographic record
Abstract
Wild blueberries (Vaccinium angustifolium Ait.) are among eastern Canada's most economically important crops. Despite this, the industry faces significant labor shortages required to harvest the over 69 000 ha of wild blueberry land each year. Automation of the wild blueberry is among the leading areas of wild blueberry research. The requirement to automate several different aspects of the harvester means that at present an operator is still required in the tractor. To determine the impacts that automation features have on an operator, and to assess the potential to replace skilled operators with unskilled ones, heart and respiration rates were monitored across various automatic, semi-automatic and manual harvesting conditions. Across both years of the study, the skilled operator experienced a 13.83% decrease in average heart rate under the fully automated condition versus the fully manual condition. Similarly, the new operator experienced a 19.03% decrease in average heart rate for the same scenario. While a conclusive determination cannot be made due to the significant interaction effect, it was likewise interesting to note that the skilled operator seemed to benefit more from the automated head adjustment while the new operator seemed to benefit more from the autosteer. Respiration rate data did not yield a conclusive trend, though the highest respiration rates were seen under the fully manual harvesting condition in all but the 2022 new operator data. In all, this study lays significant groundwork in the justification of automation for addressing the skilled labour shortage and for the eventual full automation of the wild blueberry harvester.
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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 | 0.001 | 0.003 |
| 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) | 0.001 | 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".