NAIL, a software toolset for inferring, analyzing and visualizing regulatory networks
Bibliographic record
Abstract
doi: 10.1093/bioinformatics/btu612 Bioinformatics (2015) 31(2), 277–278 The authors of the above article would like it to be known that the author affiliations should read as follows: Daniel G. Hurley1,2,3,4,*, Joseph Cursons1,4, Yi Kan Wang1,5, David M. Budden4, Cristin G. Print2,3,6 and Edmund J. Crampin1,4,7,8 1Auckland Bioengineering Institute, University of Auckland, Auckland 1001, New Zealand, 2Department of Molecular Medicine and Pathology, School of Medical Sciences, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1001, New Zealand, 3Bioinformatics Institute, University of Auckland, Auckland 1001, New Zealand, 4Systems Biology Laboratory, Melbourne School of Engineering, University of Melbourne, Victoria 3010, Australia, 5Department of Molecular Oncology, British Columbia Cancer Agency, Vancouver, Canada, 6Maurice Wilkins Centre, University of Auckland, Auckland 1001, New Zealand, 7Department of Mathematics and Statistics, University of Melbourne and 8School of Medicine, University of Melbourne, Victoria 3010, Australia
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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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.105 | 0.030 |
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".