Characterization of Aquatic Particles by Direct FTIR Analysis of Filters and Quantification of Elemental and Molecular Compositions
Bibliographic record
Abstract
This paper presents the first characterization of aquatic particles and particulate organic matter (POM) by attenuated total reflectance infrared spectroscopy (ATR-FTIR) using particles deposited on filters. Particles from 30 water samples from the St. Lawrence System (Canada) were analyzed. ATR-FTIR spectra revealed changes in numerous organic and inorganic functional group contents. Particles from marine waters contained POM enriched in amide, N-H, and aliphatic groups, while terrigenous POM had more COO(-)/COOH and aromatic groups. The spectra showed the selective degradation of amide, N-H, aliphatic, and carbohydrate-like structures during the sinking of the particles. Partial least-squares (PLS) regression of the ATR-FTIR spectra was used to quantify 12 important elemental and molecular parameters, such as amino acids, bacterial biomarkers, and degradation indices. Most parameters were quantified with good accuracy compared to conventional methods (<15% error). The spectral regions leading to the best quantifications and the PLS loadings revealed that aromatic cycles, other unsaturated structures, and COO(-)/COOH groups were degraded at a much slower rate than N-molecules, such as amino acids, and carbohydrates. Marine POM was enriched in CH(3) groups. CH(3) groups appeared highly labile and abundant in bacterial POM. ATR-FTIR represents a new and powerful method for a rapid, inexpensive, and nondestructive characterization of particles collected by filtration revealing important biogeochemical processes involving POM.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".