Variation in Phenanthrene Sorption Coefficients with Soil Organic Matter Fractionation: The Result of Structure or Conformation?
Bibliographic record
Abstract
Sorption of phenanthrene to varying soil types was investigated to better understand sorption processes. Humic acid and humin fractions were isolated from each soil sample, and sorption coefficients were measured by batch equilibration. Samples were characterized by carbon analysis and 13C cross polarization magic angle spinning (CP/ MAS) nuclear magnetic resonance (NMR) spectroscopy. Measured organic carbon-normalized sorption coefficients (Koc) of the fractions were greater in all cases when compared to the soils. The humin fractions exhibited greater Koc values than did source samples, suggesting that fractionation may reorganize organic matter in humin resulting in an increased availability of and/or more favorable sorption domains. Mass balance calculations revealed that the sum of sorption to the fractions is greater than sorption to the whole sample. The greatest difference between sorption values was found to occur with the mineral soils, suggesting that clay minerals influence the physical conformation of soil organic matter (SOM) and availability of sorption domains. The mass balance, sorption data, and a lack of consistent trends between observed Kco values and solid-state 13C NMR data suggest that the physical conformation of SOM and chemical characteristics both play important roles in sorption processes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".