Pyrolysis gas chromatography - mass spectrometry of humic substances extracted from Canadian lake sediments
Bibliographic record
Abstract
Humic substances (HS) were extracted with the aid of 0.1 M Na4P2O7 and 0.5 M NaOH solutions from the sediments of four lakes located in the Sudbury area in Ontario, Canada, namely, Tilton, Clearwater, Silver and Ramsey Lake. The humic acid (HA) and fulvic acid (FA) extracts, purified and characterized using classical methods i.e., elemental analysis, FTIR spectroscopy, and CPMAS 13C NMR (see N. Belzile, H.A. Joly, and H. Li. (1997)), were submitted to pyrolysis - gas chromatography - mass spectrometry (Py-GC-MS). The pyrolysates of the HA and FA extracts were found to be complex mixtures of at least 200 compounds. Results based on statistical analysis of the abundances of pyrolysis products (of known origin) revealed trends similar to those obtained from the classical bulk characterization techniques. The Py-GC-MS technique supported the observation, obtained from classical methods, that the chemical composition of HA extracts varied less significantly among the four lake sediments than for the FA extracts. The abundances of lignin and carbohydrate pyrolysates showed the highest variation among the FA extracts. Pyrolysis products originating from lignin, carbohydrates, proteins, and fatty acids were identified. Key words: humic substances, humic acid, fulvic acid, pyrolysis, pyrolysis gas chromatography mass spectrometry, lake sediments.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".