Temperature Sensitivity Indicates That Chlorination of Organic Matter in Forest Soil Is Primarily Biotic
Bibliographic record
Abstract
Old assumptions that chloride is inert and that most chlorinated organic matter in soils is anthropogenic have been challenged by findings of naturally formed organochlorines. Such natural chlorination has been recognized for several decades, but there are still very few measurements of chlorination rates or estimates of the quantitative importance of terrestrial chlorine transformations. While much is known about the formation of specific compounds, bulk chlorination remains poorly understood in terms of mechanisms and effects of environmental factors. We quantified bulk chlorination rates in coniferous forest soil using 36Cl-chloride in tracer experiments at different temperatures and with and without molecular oxygen (O2). Chlorination was enhanced by the presence of O2 and had a temperature optimum at 20 degrees C. Minimum rates were found at high temperatures (50 degrees C) or under anoxic conditions. The results indicate (1) that most of the chlorination between 4 and 40 degrees C was biotic and driven by O2 dependent enzymes, and (2) that there is also slower background chlorination occurring under anoxic conditions at 20 degrees C and under oxic conditions at 50 degrees C. Hence, while oxic and biotic chlorination clearly dominated, chlorination by other processes including possible abiotic reactions was also detected.
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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 | 0.000 | 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) | 0.001 | 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".