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Oviposition preference and larval performance in the exotic birch‐leafmining sawfly <i>Profenusa thomsoni</i>

2006· article· en· W2165919935 on OpenAlexfundno aff
Scott C. Digweed

Bibliographic record

VenueEntomologia Experimentalis et Applicata · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaEntomological Society of CanadaTree Research and Education Endowment Fund
KeywordsSawflyTenthredinidaeBiologyIntraspecific competitionLarvaHymenopteraCompetition (biology)BotanyZoologyEcology

Abstract

fetched live from OpenAlex

Abstract This study used experiments at several spatial scales to determine whether (1) intraspecific competition occurs among larvae of the leafmining sawfly Profenusa thomsoni (Konow) (Hymenoptera: Tenthredinidae) on birch ( Betula spp.), (2) oviposition site preferences of P. thomsoni maximize offspring performance, and (3) early‐season damage by external folivores or the leafminer Fenusa pumila Leach (Hymenoptera: Tenthredinidae) affects oviposition preferences or larval performance of P. thomsoni . Larval P. thomsoni competed at natural densities; survival and weight of larvae were reduced under crowded conditions. Despite this, females of P. thomsoni tended to lay eggs on leaves already bearing eggs from other females and discriminated only weakly among leaves of different sizes on a branch. Both damage by F. pumila and artificial damage to leaves early in the season decreased survival of P. thomsoni larvae on the same branch, and ovipositing P. thomsoni females avoided damaged leaves but not other leaves on the same branch. In general, oviposition choices by P. thomsoni reduced larval survival. Possible reasons for the lack of a strong preference–performance relationship in P. thomsoni are discussed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.704
Threshold uncertainty score0.234

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.084
GPT teacher head0.240
Teacher spread0.156 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations35
Published2006
Admission routes1
Has abstractyes

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