Bibliographic record
Abstract
This invited commentary covers the period 1997-2012 and has seen changes in terminology that progressed from "basic" and "applied" to "translational" research. In the context of Bioengineered, these changes map readily onto the processes of identifying microbial characteristics appropriate for specific applications, isolation of suitable cultures, strain or genome manipulation and exploitation of these or their metabolomes across a range of settings. To a great degree, this commentary and my career reflect an engagement with molecular microbiology and the trialling of bacteria and derived constructs in applications ranging from intensive-scale crop protection to amelioration of gastrointestinal disease. This engagement began with laboratory and field evaluations of biocontrol, specifically use of pseudomonads effective against nematode and fungal plant pathogens, characterization of mechanisms mediating beneficial effects of probiotic lactobacilli and bifidobacteria and assessment of functional foods in multinational clinical trials relating to inflammatory bowel disease. Subsequent work focused on (1) intellectual property (IP)-based medical devices for localized delivery of systemically toxic and gene cancer therapies; (2) growth of the science base supporting expansion of a multinational business including company acquisitions; (3) complementing existing inter-institutional research capabilities through development of a national industry-led collaboration; and, most recently, (4) strategic research programs at Ireland's newest medical school. My activities as outlined above parallel two distinct aspects of translational research: (1) involvement in knowledge-driven (commercial and research) organizations that brought together necessary resources and infrastructure and (2) availability of scale research funding from European Framework and Irish national programs.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".