Raw proteomics data for Multi-Omics Analysis Reveals Diapause-Associated Lipid Remodeling in the Fat Body of Colorado Potato Beetle
Bibliographic record
Abstract
ProteomicsProteins were extracted using 2.5% sodium dodecyl sulfate, 5% β-mercaptoethanol, and subjected to in-solution digest liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis at the University of Victoria Genome British Columbia Proteomics Centre, Canada. Samples were denatured, reduced, alkylated, and digested using a Preomics iST Sample Preparation kit (Preomics GmbH, Martinsried, Germany) according to the manufacturer's protocol. Samples were re-suspended in Load buffer at a concentration of 1 µg/µL. Peptide mixtures (~1 µg) were separated by on-line reverse phase chromatography using a Thermo Scientific EASY-nLC 1000 system with a reversed-phase pre-column and an in-house prepared reverse phase nano-analytical column, coupled on-line with an Orbitrap Fusion Tribrid mass spectrometer (Thermo Fisher Scientific, San Jose, CA) equipped with a Nanospray Flex NG source. Solvents used were 2% acetonitrile, 0.1% formic acid (Solvent A), and 90% acetonitrile, 0.1% formic acid (Solvent B). Samples were separated by a 140-minute gradient and analyzed using the Orbitrap Fusion instrument with specified parameters for iontrap (IT-MS/MS) with HCD fragmentation. Raw data files were generated using XCalibur 4.2.28.14 (Thermo Scientific) software and analyzed with Proteome Discoverer 2.2.0.388 software suite (Thermo Scientific).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".