(3<I>Z</I>,6<I>Z</I>,9<I>Z</I>,12<I>Z</I>,15<I>Z</I>)-Pentacosapentaene and (<I>Z</I>)-11-Hexadecenyl Acetate: Sex Attractant Blend for <I>Dioryctria amatella</I> (Lepidoptera: Pyralidae)
Bibliographic record
Abstract
In 2006-2008, we tested (3Z,6Z,9Z,12Z,15Z)-pentacosapentaene (pentaene) with the pheromone components (Z)-11-hexadecenyl acetate (Z11-16:Ac) and (Z)-9-tetradecenyl acetate (Z9-14:Ac), as sex attractants for four sympatric species of coneworms, Dioryctria Zeller (Lepidoptera: Pyralidae) in slash (Pinus elliottii Engelm.) and loblolly pine (Pinus taeda L.) seed orchards in Georgia and Louisiana, respectively. The addition ofpentaene increased catches of male southern pine coneworm, Dioryctria amatella (Hulst), in wing traps baited with Z11-16:Ac, whereas catches of Dioryctria disclusa Heinrich in traps baited with Z9-14:Ac were unaffected by the addition of pentaene. The effect of pentaene on male Dioryctria merkeli Mutuura & Munroe was inconsistent. In 2006, pentaene seemed to inhibit attraction of D. merkeli to traps baited with Z9-14:Ac, whereas in a subsequent trial in 2008, moths were equally attracted to Z9-14:Ac with or without the pentaene. We caught too few Dioryctria clarioralis (Walker) in any experiment for meaningful analyses. Our field results with pentaene and the unresolved complexity of the taxonomy, ecology, and management of southern coneworms support the need for a comprehensive examination of the chemical ecology of Dioryctria spp.
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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 | 0.000 | 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 | 0.001 |
| 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".