Deep-sequencing of the Peach Latent Mosaic Viroid Reveals New Aspects of Population Heterogeneity (Suppl. data)
Bibliographic record
Abstract
Supplementary data for the article: 'Deep-sequencing of the Peach Latent Mosaic Viroid Reveals New Aspects of Population Heterogeneity' Jean-Pierre Sehi Glouzon1,2,a, François Bolduc2,a , Rafael Najmanovich2, Shengrui Wang1, Jean-Pierre Perreault2* 1Département d’informatique, Faculté des sciences, Université de Sherbrooke, Sherbrooke, Québec, J1H 5N4, Canada. 2RNA Group/Groupe ARN, Département de biochimie, Faculté de médecine et des sciences de la santé, Pavillon de Recherche Appliquée au Cancer, Université de Sherbrooke, Sherbrooke, Québec, J1H 5N4, Canada. aThese authors contributed equally to this study. Running title: Genetic Variability of PLMVd Submitted: October 3rd, 2012 *Corresponding author: Jean-Pierre Perreault, Ph.D (Jean-Pierre.Perreault@usherbrooke.ca) Phone: (819) 564-5315; Fax: (819) 564-5340 The article is currently under review at PLoS ONE and the preprint released via arXiv at: http://arxiv.org/abs/1212.0413
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 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.104 | 0.057 |
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".