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Record W4394121243 · doi:10.6084/m9.figshare.156055

Quantitative differences in protein levels between mountain pine beetle, Dendroctonus ponderosae, larvae collected from host lodgepole pine trees in September and November

2013· dataset· en· W4394121243 on OpenAlexaboutno aff
Tiffany R. Bonnett, Jeanne A. Robert, Caitlin Pitt, Jordie D. Fraser, Luke J. Spooner, Christopher I. Keeling, Jöerg Bohlmann, Dezene P.W. Huber

Bibliographic record

VenueFigshare · 2013
Typedataset
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsnot available
Fundersnot available
KeywordsMountain pine beetleDendroctonusHost (biology)Pinus contortaLarvaForestryBiologyEcologyGeographyBark beetleBark (sound)

Abstract

fetched live from OpenAlex

Proteomics data (XLSX file) for overwintering mountain pine beetle, Dedroctonus ponderosae, larvae live-collected from lodgepole pine hosts in September and November. Collections were made near to Valemont, British Columbia, Canada. Please cross reference the following TXT file of FASTA nucleotide data to further investigate any particular protein: http://dx.doi.org/10.6084/m9.figshare.156056 Similar proteomic data for spring-collected larvae are available here: http://dx.doi.org/10.6084/m9.figshare.156054 These data pertain to the open access paper: Tiffany R. Bonnett, Jeanne A. Robert, Caitlin Pitt, Jordie D. Fraser, Christopher I. Keeling, Jörg Bohlmann, Dezene P.W. Huber, Global and comparative proteomic profiling of overwintering and developing mountain pine beetle, Dendroctonus ponderosae (Coleoptera: Curculionidae), larvae, Insect Biochemistry and Molecular Biology, Volume 42, Issue 12, December 2012, Pages 890-901, ISSN 0965-1748, 10.1016/j.ibmb.2012.08.003. The paper can be accessed here: http://www.sciencedirect.com/science/article/pii/S0965174812001221

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.002

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 head0.041
GPT teacher head0.258
Teacher spread0.217 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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
Published2013
Admission routes1
Has abstractyes

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