Characterization of organic matter of plants from lakes by thermal analysis in a N2 atmosphere
Bibliographic record
Abstract
Organic matter (OM) has been characterized using thermal analysis in O2 atmospheres, but it is not clear if OM can be characterized using slow thermal degradation in N2 atmospheres (STDN). This article presents a new method to estimate the behavior of OM in anaerobic environment. Seventeen different plants from Tai Lake (Ch: Taihu), China were heated to 600 °C at a rate of 10 °C min(-1) in a N2 atmosphere and characterized by use of differential scanning calorimetry (DSC) and thermal gravimetric analysis (TGA). DSC chromatograms were compared with 9 standard compounds. Seven peaks were observed in DSC chromatograms, 2 main peaks strongly correlated with biochemical indices, and one main peak was a transitional stage. Energy absorbed by a peak at approximately 200 °C and total organic carbon were well correlated, while energy absorbed at approximately 460 °C was negatively correlated with lignin content. Presence of peaks at approximately 350 and 420 °C varied among plant biomass sources, providing potential evidence for biomass identification. Methods of STDN reported here were rapid and accurate ways to quantitatively characterize OM, which may provide useful information for understanding anaerobic behaviors of natural organic matters.
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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) | 0.000 | 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 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".