Training the Biofilm Generation—a Tribute to J. W. Costerton
Bibliographic record
Abstract
Although bacterial growth on surfaces was described by earlymicrobiologists, including Claude ZoBell (61), the term “bio-film ” (38) and the importance of adherent bacteria in nature and disease did not gain full prominence in the scientific community until the work of J. W. (Bill) Costerton’s group and the numerous laboratories that he collaborated with (11, 13, 16). During his lifetime (31), Bill’s boundless energy enabled him to push the concept of biofilms as a key to understanding how bacteria inter-act with the environment, be it rock surfaces, heat exchangers, medical devices, or human tissues. Bill was a highly effective com-municator through his prolific writing (more than 600 peer-re-viewed publications) and also through his public speaking, which conveyed exciting scientific concepts to audiences ranging from scientists to medical doctors, engineers, the U.S. Senate, and even the general public. For the past 2 decades, there have been numer-ous biofilm conferences, including the ones sponsored by the American Society for Microbiology (ASM), European and Asian microbiology societies, and biofilm research institutions and cen-ters around the world. Full validation that research on biofilms must be done was when the U.S. National Institutes of Health publicly announced, “Biofilms are medically important, account-ing for over 80 % of microbial infections in the body ” (program
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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 | 0.018 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.009 | 0.032 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".