MétaCan
Menu
Back to cohort
Record W2077293609 · doi:10.1007/s11295-009-0220-2

Spruce proteome DB: a resource for conifer proteomics research

2009· article· en· W2077293609 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueTree Genetics & Genomes · 2009
Typearticle
Languageen
FieldChemistry
TopicAdvanced Proteomics Techniques and Applications
Canadian institutionsUniversity of ManitobaCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaGenome British ColumbiaGenome Canada
KeywordsBiologyProteomeProteomicsComputational biologyIdentification (biology)DNA sequencingSequence databaseSequence (biology)GenomeProtein sequencingResource (disambiguation)BioinformaticsDNAGeneticsEcologyComputer sciencePeptide sequenceGene

Abstract

fetched live from OpenAlex

Proteomics research is hampered in many organisms due to a lack of an appropriate reference genome sequence that can be used in the interpretation of tandem mass spectrometry data for the identification of proteins. Public DNA sequence repositories have grown to considerable size and can, in most cases, serve to provide at least partial interpretation of a large-scale proteomics dataset. However, when species-specific sequences or sequences from a closely related species are available, a boutique sequence database can provide considerable increases in specificity, confidence, and completeness of protein identification. Here, we describe the development of a protein database from a large-scale expressed sequence tag and full-length complementary DNA sequencing project in the economically and ecologically important spruce ( Picea ) genus.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.467
Threshold uncertainty score1.000

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)0.0000.000

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 head0.053
GPT teacher head0.357
Teacher spread0.305 · 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