Involvement and contribution to virulence of Endo-Beta-1,4-Xilanases of Fusarium Graminearum during host infection
Bibliographic record
Abstract
Liquid Chromatography coupled to Tandem Mass Spectrometry (LC-MS/MS) based methods are currently the top choice for high-throughput, quantitative measurements of the proteome. While traditional proteomics LC-MS/MS methods can suffer from issues such as low reproducibility and quantitative accuracy due to its stochastic nature, recent improvements in acquisition protocols have resulted in methods that can overcome these challenges. Data-independent acquisition (DIA) is a novel mass spectrometric method that does so by using a deterministic acquisition strategy. These new approaches will allow researchers to apply MS on more complex samples, however, existing heuristic and expert-knowledge based methods will struggle with keeping pace of the increasing complexity of the resulting data. Deep learning (DL) based methods have been shown to be more adept at handling large amounts of complex data than traditional methods in many other fields, such as computer vision and natural language processing. Proteomics is also entering a phase where the size and complexity of the data will require us to look towards scalable and data-driven DL pipelines.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".