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Record W18470083 · doi:10.1139/g94-055

How market research can help grow your business

2008· article· en· W18470083 on OpenAlexvenueno aff
Doren Chadee, Karen W. Miller

Bibliographic record

VenueGenome · 2008
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicService and Product Innovation
Canadian institutionsnot available
Fundersnot available
KeywordsCompetitor analysisBusinessMarketingMarket researchMarket analysisLoyalty business modelCompetitive advantageLoyaltyIndustrial organizationService (business)

Abstract

fetched live from OpenAlex

The existence of sibling species in the Old World screwworm fly Chrysomya bezziana would raise serious problems in eradicating this pest if it entered Australia. Cytogenetic variation in C. bezziana was investigated by analyzing pupal trichogen polytene chromosomes. Natural populations of C. bezziana spanning its range from southern Africa to Papua New Guinea were examined as well as hybrids between a New Guinea laboratory strain and natural populations. No evidence of sibling species was found. All populations exhibited the same basic banding pattern as the standard sequence established from a Papua New Guinea strain. Extensive asynapsis of chromosome homologues was found in some hybrid crosses and was therefore measured in all populations and hybrids to detect systematic variation. Asynapsis levels in most hybrids could not be statistically distinguished from those present in the parent populations except for crosses between populations at the ends of the range. This result does not permit asynapsis levels to be used in establishing the origin of introduced flies by estimating their distance from known populations. One inversion polymorphism and six band polymorphisms spread over three chromosomes were analyzed. Populations in each sampled region had characteristic combinations of band polymorphisms. This may offer a diagnostic method for determining the origin of flies accidentally introduced to Australia.

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)0.001
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.202
Threshold uncertainty score0.521

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.105
GPT teacher head0.272
Teacher spread0.168 · 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 designNot applicable
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

Citations0
Published2008
Admission routes1
Has abstractyes

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