Poster and Presentation Abstracts Entomological Society of Ontario Annual General Meeting
Bibliographic record
Abstract
Evolutionary biologists strive to understand the patterns that have shaped life in our planet and the processes that create and maintain biodiversity.Central to this scientific inquiry are phylogenies.The recently developed 'omics' approaches give us the possibility to pursue new avenues in biodiversity research and the privilege to revisit the insect Tree of Life using data that was previously unavailable.However, not all parts of the genome are equally useful for reliable phylogenetic inference, and we now require new statistical approaches to identify genomic regions that we can adequately model and to develop rigorous protocols for robust estimation of phylogeny.Our current context of climate change and biodiversity loss now require speedy, specimen-based research more urgently than ever, with large-scale biodiversity surveys and natural history collections playing a key role in expanding our understanding of insect diversity, ecology, and natural history.posters abstracts assessing omnivorous predators (hemiptera: miridae) for their potential use as biological control agents of greenhouse tomato pests.C. demers*, R. Labbé, and S. VanLaerhoven *demers51
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.337 | 0.085 |
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".