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Biology of the gumleaf skeletoniser, <i>Uraba lugens</i> Walker (Lepidoptera: Noctuidae), in the southern jarrah forest of Western Australia

2002· article· en· W2073873144 on OpenAlexaff
Janet D Farr

Bibliographic record

VenueAustralian Journal of Entomology · 2002
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsDepartment of Environment and Conservation
Fundersnot available
KeywordsVoltinismBiologyFecundityPopulationEcologyLepidoptera genitaliaPhenologyLarvaDemography

Abstract

fetched live from OpenAlex

Abstract The biology of Uraba lugens Walker, gumleaf skeletoniser (GLS), in the southern jarrah ( Eucalyptus marginata Donn ex Smith) forest of Western Australia is examined with respect to: (1) distribution of egg rafts and larvae in the host canopy; (2) survival on different food plants; (3) phenology; (4) mortality; and (5) fecundity. Limited preference for height was found for eggs and larvae within the jarrah canopy. Both jarrah and marri ( Corymbia calophylla (Lindl.) K.D. Hill and L.A.S. Johnson) were intermediate hosts within the host range for GLS when compared with more preferred hosts from eastern Australia. A simulation model, used to determine phenology, showed GLS to be bivoltine in Perth and univoltine in the southern jarrah forest near Manjimup, with capacity for the univoltine form to change to a bivoltine cycle following two consecutive warm winters. Six parasitoids were isolated from populations in south‐west Western Australia. The eulophid Euplectrus sp. and an unknown mortality agent were the predominant causes of the population decline in 1988, following an outbreak during 1982–88. Fecundities were significantly different between the outbreak and non‐outbreak periods. Furthermore, fecundity for the southern jarrah forest population was significantly higher than measured fecundities of bivoltine South Australian populations.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.214
Threshold uncertainty score0.999

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.037
GPT teacher head0.256
Teacher spread0.220 · 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.

Study designObservational
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

Citations34
Published2002
Admission routes1
Has abstractyes

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