Microbes and the Water Industry: What to Expect by 2050
Bibliographic record
Abstract
and a member of the OIWC Today, the idea that there are microscopic organisms in water is almost magical when youngsters first learn of it, but then it becomes an understood part of reality, like gravity.We have come a long way in our understanding of microorganisms since they were first described by Antonie van Leeuwenhoek in 1676 as "many living animalcules, very prettily a-moving."As time passes, our understanding of microorganisms continues to grow at a near-exponential rate, and the coming decades will no doubt hold many exciting discoveries.The mission of AWWA's Organisms in Water Committee (OIWC), which is part of the association's Water Quality and Technology Division (WQTD), is to synthesize and communicate the current state of the science on organisms in water, including pathogens, toxin producers, invasive species, indicators, and nuisance organisms (for details, see https://doi.org/10.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".