Avian grape predation at vineyards in South Western Ontario: evaluating fruit preferences and non-invasive deterrent methods
Bibliographic record
Abstract
Avian grape predation at vineyards in South Western Ontario: evaluating fruit preferences and non-invasive deterrent methods Most fruit colours evolved to attract seed disperses, which causes problems in agricultural areas where damage by birds annually costs growers thousands of dollars per hectare. I will be reviewing what is currently known in this field and I will introduce my proposed Masters research on this topic. The methods of avian control techniques through the years has changed based on the results of past methods and development of newer available technology. Past work quantifying the damage of crops in vineyards and orchards goes back to the 1960’s and lends understanding to some of the factors that influence the degree of avian predation of crops. My Master’s research will quantify avian damage in local vineyards in South Western Ontario and determine whether the intensity of avian grape predation corresponds to fruit colour and/or sugar content. My thesis will also test the efficiency of both current and novel avian deterrent methods in vineyards over the course of the 2019 ripening season. My work has both evolutionary and agricultural implications: it will aid in understanding what drives avian fruit colour preferences and how particular fruits co-evolved to become more attractive to these species, and will help develop the most efficient/ non-invasive management techniques to deter avian crop predation.
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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 | 0.001 |
| 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 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".