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Additional file 4 of Integrative analysis of green ash phloem transcripts and proteins during an emerald ash borer infestation

2024· dataset· en· W6958428192 on OpenAlexaff

Bibliographic record

VenueFigshare · 2024
Typedataset
Languageen
FieldComputer Science
TopicData Analysis with R
Canadian institutionsUniversité LavalNatural Resources Canada
Fundersnot available
KeywordsTable (database)GeneTranscriptomeGene expressionKEGGPhloem

Abstract

fetched live from OpenAlex

Additional file 4: Table S2. The putative functions of differentially expressed transcripts found in high-low comparisons. Table S3. The putative functions of differentially expressed transcripts found in high-medium comparisons. Table S4. The putative functions of differentially expressed transcripts found in medium-low comparisons. Table S5. The putative functions of differentially expressed proteins found in high-low comparisons. Table S6. Differentially expressed transcripts found in the high-low comparison that were also differentially expressed in EAB treatment vs control (TvC) experiment from Lane et al. 2016 [19]. Table S7. Differentially expressed transcripts found in the high-medium comparison that were also differentially expressed in EAB treatment vs control (TvC) experiment from Lane et al. 2016 [19]. Table S8. Differentially expressed transcripts found in the medium-low comparison that were also differentially expressed in EAB treatment vs control (TvC) experiment from Lane et al. 2016 [19]. Table S9. Differentially expressed proteins found in the high-low comparison that were also differentially expressed in EAB treatment vs control (TvC) experiment from Lane et al. 2016 [19]. Table S10. Differentially expressed proteins/transcripts found in the integrative analysis that were also differentially expressed in EAB treatment vs control (TvC) experiment from Lane et al. 2016 [19]. Table S11. Differentially expressed transcripts found in the high-low and high-medium comparison that were also identified as EAB resistance genes by Kelly at al. 2020.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.866
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.8660.000

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 head0.019
GPT teacher head0.257
Teacher spread0.239 · 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 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
Published2024
Admission routes1
Has abstractyes

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