Bibliographic record
Abstract
“Death of microorganisms, exposed to many sublethal treatments, is a gradual process which could be reversed under proper conditions if the reaction has not progressed too far.” This statement made by Rahn and Barnes 1 and Rahn 2 in 1932 clearly suggested that some physical and chemical agents in a sublethal dosage inflict reversible injuries in microbial cells. Since the beginning of this century many microbiologists recognized that pure cultures of both vegetative bacteria and bacterial spores subjected to a sublethal dosage of heat, UV light, mercuric chloride, and other agents suffered cellular damages and became more exacting in their nutritional need for subsequent growth. 3–8 Microbiologists involved in the development of methods and media for the quantitative evaluation of the microbiological qualities of heat-processed foods that contained different types of microorganisms observed that supplementing nonselective types of media with yeast extracts, milk, etc., improved recovery. 9–13 It was recommended that “This should be considered in the formulation of media for the enumeration of bacteria in heated food products and in experiments concerned with the effects of heat on microorganisms.” 12 Other researchers also observed that indicator, pathogenic, and other bacteria in frozen foods also were not effectively detected, either by nonselective or selective media, due to reversible injury. 14 , 15 In 1959 Straka and Stokes 16 showed that certain fractions of Escherichia coli and Pseudomonas spp. that survived freezing and thawing were metabolically injured and needed several types of peptides to reverse their injury. From the 1960s to the early 1980s many laboratories, mainly in the U.S., the U.K., Japan, Canada, and the Netherlands, conducted research on the sublethal injury of indicator and food and water-borne pathogens. 17–19 These studies indicated that most physical and chemical treatments, when applied to sublethal dosages, could inflict injury on microbial cells found in food and water (see Tables 1 and 2 ). These cells, although constituted as part of the viable microbial population, have many altered physiological characteristics, and a specific method used in the microbiological evaluation of a sample could make the injured fraction detectable or undetectable.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.418 | 0.269 |
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".