Using Py-GC/MS to fingerprint additives associated with paper mill effluent toxicity episodes
Bibliographic record
Abstract
Understanding the cause of effluent toxicity is an important requirement for its prevention, remediation and return to compliance. One component of the strategy entails identification and fingerprinting of additives or components in additives that may be the cause of the toxicity episodes. A number of additives used in pulp and papermaking are polymeric compounds that are suspect in effluent toxicity. Their analysis and detection is difficult as they are not amenable to analysis by normal techniques applicable to mill effluents such as gas chromatography. Py-GC/MS is a powerful analytical technique that can be used to fingerprint these additives. The presence of the additives is confirmed by fingerprint pyrograms of the additives (or components in the formulations of the additives) in conjunction with mass spectrometry. The technique has been used to fingerprint and quantify polymeric additives associated with mill effluent toxicity episodes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".