Organic Matter Chlorination Rates in Different Boreal Soils: The Role of Soil Organic Matter Content
Bibliographic record
Abstract
Transformation of chloride (Cl(-)) to organic chlorine (Cl(org)) occurs naturally in soil but it is poorly understood how and why transformation rates vary among environments. There are still few measurements of chlorination rates in soils, even though formation of Cl(org) has been known for two decades. In the present study, we compare organic matter (OM) chlorination rates, measured by (36)Cl tracer experiments, in soils from eleven different locations (coniferous forest soils, pasture soils and agricultural soils) and discuss how various environmental factors effect chlorination. Chlorination rates were highest in the forest soils and strong correlations were seen with environmental variables such as soil OM content and Cl(-) concentration. Data presented support the hypothesis that OM levels give the framework for the soil chlorine cycling and that chlorination in more organic soils over time leads to a larger Cl(org) pool and in turn to a high internal supply of Cl(-) upon dechlorination. This provides unexpected indications that pore water Cl(-) levels may be controlled by supply from dechlorination processes and can explain why soil Cl(-) locally can be more closely related to soil OM content and the amount organically bound chlorine than to Cl(-) deposition.
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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 | 0.001 | 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".