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Record W2271370074 · doi:10.4039/tce.2015.40

Bioclimatic approach to assessing the potential impact of climate change on wheat midge (Diptera: Cecidomyiidae) in North America

2015· article· en· W2271370074 on OpenAlexafffund
O. Olfert, R.M. Weiss, R. H. Elliott

Bibliographic record

VenueThe Canadian Entomologist · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsSaskatoon Medical Imaging
FundersMinistry of Agriculture - Saskatchewan
KeywordsCecidomyiidaeMidgeSecalePoaceaeAbundance (ecology)BiologyTriticalePEST analysisAgronomyEcologyClimate changeBotanyLarva

Abstract

fetched live from OpenAlex

Abstract Wheat midge, Sitodiplosis mosellana (Géhin) (Diptera: Cecidomyiidae), Palaearctic in origin, is thought to have been introduced into North America in the early 1800s. It is a major pest of spring wheat ( Triticum aestivum Linnaeus (Poaceae)), durum wheat ( T. durum Desfontaines), triticale (X- Triticosecale ), and, to a lesser extent, spring rye ( Secale cereale Linnaeus (Poaceae)) throughout the northern Great Plains. Climate is the principal factor regulating the distribution and abundance of most insects. A bioclimate simulation model was developed to explain the current distribution and abundance of S. mosellana . The current distribution for North America, Europe, and Asia was consistent with model projections. General circulation model scenarios (CSIRO-MK 3.0 and MIROC-H) for the 2030 and 2070 time periods were applied to the bioclimate simulation model of S. mosellana to assess the potential impact of changing climates on their distribution and relative abundance. Potential changes to relative abundance and distribution were most sensitive to time period, as opposed to climate change scenario. Differences between the MIROC-H and CSIRO-MK 3.0 models were restricted to particular regions in North America. The study found that the range and abundance of S. mosellana , and associated crop risk, was predicted to expand in a northerly direction and contract across the present southern limits.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.576
Threshold uncertainty score0.526

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 head0.046
GPT teacher head0.272
Teacher spread0.226 · 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 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

Citations25
Published2015
Admission routes2
Has abstractyes

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