Alternative methods in tracking sources of microbial contamination in waters
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A key factor in the management and remediation of impaired ground- and surface water is the ability to distinguish the sources of faecal contamination. Several approaches have been adopted as microbial source tracking methods (MST), which are generally classified as culturing, phenotypic, genetic, and chemical MST. None of the techniques used thus far can be considered a standard; important factors, such as the statistical correlation between the source and the faecal indicator and the understanding of the environmental fate of the faecal pollutants, still need attention. The most promising MST methods available today are based on the genetic fingerprinting of faecal micro-organisms. However, research is very active also in the investigation of pharmaceuticals and personal care products discharged in the environment together with faecal waste.An updated overview of MST methods to distinguish human from animal sources of faecal pollution is presented here, focusing particularly on the potentialities of new chemical tracers.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 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 it