Thrips Species Composition in Ontario Greenhouse Floriculture: Innovative Identification Tools and Implications for Integrated Pest Management
Bibliographic record
Abstract
Proper species identification is the keystone of successful integrated pest management (IPM). However, efforts to identify thrips species in Canadian greenhouses have not been formally made since the 1980s. In response to recent increases in crop damage, we sampled thrips communities from eight commercial floriculture greenhouses in the Niagara region (Ontario, Canada) from May until August 2016. Selected sites were revisited in 2017, 2018, and 2019 to determine changes in species composition over time. Western flower thrips (Frankliniella occidentalis (Pergande)), along with onion thrips (Thrips tabaci Lindeman), constituted the majority of species found. Other pest species (less than 8% of specimens across all sampling years) included poinsettia thrips (Echinothrips americanus Morgan), chrysanthemum thrips (Thrips nigropilosus Uzel), and Frankliniella fusca (Hinds). Further investigations of thrips outbreaks in Ontario from 2016 to 2023 revealed other important species, including Thrips parvispinus (Karny), Hercinothrips femoralis (Reuter), and Scirtothrips dorsalis Hood. The current biocontrol strategies used in Ontario floriculture crops for western flower thrips do not adequately control onion thrips or other thrips pests in ornamental crops, making identification a fundamental step in determining whether biocontrol or chemical control strategies should be implemented. However, traditional taxonomic keys are inaccessible to non-specialists due to their technical difficulty. Using the data gathered in these surveys, we developed a simplified, illustrated identification key for use by growers and IPM consultants.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".