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Record W2142627948 · doi:10.1071/zo10034

Positive and negative effects of phoretic mites on the reproductive output of an invasive bark beetle

2010· article· en· W2142627948 on OpenAlexaff
Lisa K. Hodgkin, Mark A. Elgar, Matthew R. E. Symonds

Bibliographic record

VenueAustralian Journal of Zoology · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsBC Research (Canada)
FundersAustralian Research CouncilUniversity of MelbourneCommonwealth Scientific and Industrial Research Organisation
KeywordsBiologyMiteBark beetleAcariHost (biology)ZoologyOffspringBark (sound)AcariformesLarvaReproductionEcologyBotany

Abstract

fetched live from OpenAlex

When multiple species coexist upon a single host, their combined effect on the host can be unpredictable. We explored the effect of phoretic mites on the reproductive output of the five-spined bark beetle, Ips grandicollis. Using correlative approaches and experimental manipulation of mite numbers we examined how mite load affected the number, size and condition of bark beetle offspring produced. We found that mites have both negative and positive consequences on different aspects of bark beetle reproduction. Females from which mites were removed were more fecund and produced larger offspring than females with mites, implying a cost of mite loads. However, when mites were present on females, those bearing the highest mite loads produced offspring that were larger and in better condition, indicating a beneficial effect of mites. These data suggest that phoretic interactions between mites and bark beetles differ over the course of the host’s lifespan, with either the mites interacting in different ways with different life stages of the host (parasitic on adult, mutualistic with larvae), and/or the beetles being host to different mite assemblages over their lifetime.

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)0.001
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 score0.588
Threshold uncertainty score0.412

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.008
GPT teacher head0.222
Teacher spread0.215 · 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

Citations17
Published2010
Admission routes1
Has abstractyes

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