An assessment of re‐randomization methods in bark beetle (Scolytidae) trapping bioassays
Bibliographic record
Abstract
Abstract 1 Numerous studies have explored the role of semiochemicals in the behaviour of bark beetles (Scolytidae). 2 Multiple‐funnel traps are often used to elucidate these behavioural responses. Sufficient sample sizes are obtained by using large numbers of traps to which treatments are randomly assigned once, or by frequent collection of trap catches and subsequent re‐randomization of treatments. 3 Recently, there has been some debate about the potential for trap contamination to occur when semiochemical treatments (baits), and not trap‐treatment units (traps and baits), are re‐randomized among existing traps. Due to the volatility of many semiochemicals, small levels of contamination could potentially confound results. 4 A literature survey was conducted to determine the frequency of re‐randomizing semiochemical treatments (baits) vs. trap‐treatment units (traps and baits) in scolytid trapping bioassays. An experiment was then conducted to determine whether differences in the response of Dendroctonus brevicomis LeConte to attractant‐baited traps exist between the two methods. 5 The majority of papers examined reported use of a large number of fixed replicates (traps) rather than re‐randomization of treatments at frequent intervals. Seventy‐five percent of papers for which re‐randomization methods could be determined reported relocation of semiochemical treatments (baits) only. 6 No significant differences in trap catch were observed among multiple‐funnel traps aged with D. brevcomis baits (Phero Tech Inc., Canada) for 0, 30 and 90 days, suggesting that contamination did not influence the results. 7 It is concluded that re‐randomizing baits is a viable cost‐effective option to re‐randomizing trap and bait units.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".