Introduction to the special issue: “Natural Product Discovery and Development in the Genomic Era: 2019”
Bibliographic record
Abstract
This special issue of the Journal of Industrial Microbiology and Biotechnology contains reviews, original articles, and perspectives describing recent scientific advances in natural product discovery and development. The volume is composed primarily of information presented at the 2nd International Conference on “Natural Product Discovery and Development in the Genomic Era” held in Clearwater, Florida, in January of 2018. The conference was co-sponsored by the Society for Industrial Microbiology (SIMB), the Korean Society for Microbiology and Biotechnology (KMB), and the Society for Actinomycetes Japan (SAJ), and was attended by 175 scientists from North America (USA and Canada), South America (Brazil and Chile), Asia (Japan, South Korea, and China), and Europe (Czech Republic, Denmark, Germany, Italy, Netherlands, Spain, Switzerland, and United Kingdom). The Honorary Co-chairs for the meeting were Professors Heinz Floss and Christopher Walsh. This Special Issue is dedicated to Professors Floss and Walsh for their outstanding contributions to the understanding of the fundamental enzymology of natural product biosynthesis in microorganisms that has helped establish the basis for current advances on genome mining and combinatorial biosynthesis for drug discovery. Their contributions are described in more detail in the accompanying Dedication prepared by Rolf Müller and Gerry Wright. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.027 | 0.020 |
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".