Biochemical and Ecological Control of Geosmin and 2-Methylisoborneol in Source Waters
Bibliographic record
Abstract
The majority of all biologically caused taste-and-odor outbreaks in drinking water characterized worldwide are caused by microbial production of (Ϫ)-geosmin [(Ϫ)-(4S,4aS,8aR)-4,8adimethyloctahydronaphthalen-4a-ol] and (Ϫ)-2-methylisoborneol (2-MIB) {(1R-exo)-1,2,7,7-tetramethylbicyclo[2.2.1]heptan-2-ol}.Since they were first identified in the early 1960s, these two earthy-muddy-smelling metabolites have been the focus of considerable research, which has collectively produced over 400 scientific articles, reports, websites, and conference proceedings.Yet despite this substantial body of knowledge, geosmin and 2-MIB remain poorly understood throughout much of the water industry, and misconceptions which impede the prediction, treatment, and control of these volatile organic compounds (VOCs) persist.This paper reviews salient aspects of our current knowledge on the sources and properties of geosmin and 2-MIB which are essential to understanding and managing drinking water malodors.In particular, we highlight some key factors regulating the storage and release of these compounds by cells.These important factors are often overlooked and may contribute to some of the apparent ambiguity of many taste-and-odor outbreaks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".