Fast Quantification of Humic Substances and Organic Matter by Direct Analysis of Sediments Using DRIFT Spectroscopy
Bibliographic record
Abstract
A simple method based on diffuse reflectance coupled with infrared Fourier transform spectroscopy (DRIFTS) has been developed for the quantification and the characterization of sedimentary (or soil, peat, etc.) humic substances. Under optimized conditions, the quantification of humic substances or total organic matter is possible with DRIFTS at a frequency of 2930 cm(-1) using whole dry sediment samples. A study of the operational parameters that affect the DRIFTS signal shows the importance of normalizing analysis conditions, especially the diffuse reflectance accessory alignment, the particle size and compaction, and the homogeneity of the powdered samples, to obtain reproducible quantitative analyses. The quantification of total humic substances by DRIFTS correlates well with the concentrations determined using classical extraction methods. DRIFTS analysis requires only a few minutes instead of tedious extractions of humic substances. Moreover, the distribution of total organic matter and of fulvic acids, humic acids, and humin can also be obtained. Analysis of natural samples indicates that a calibration using humic material representative of the studied area provides the most accurate quantification. The fast screening of organic matter fractions by DRIFTS on intact natural samples provides useful quantitative and qualitative information that can be used in environmental or monitoring studies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".