Influence of environmental settings, including vegetation, on speciation of the redox-sensitive elements in the sediments of monomictic Lake Kinneret
Bibliographic record
Abstract
L. into the sediments on cycling of the redox-sensitive elements in Lake Kinneret. We measured roots content, porosity, and chemical parameters including pH, sulfur, iron and manganese speciation in the sediments from the shore, littoral and sublittoral zones with and without vegetation. Our results show that at ≥ 12 m water depth, the upper 10 cm of the sediments are affected by the active sulfur cycling with concentrations of hydrogen sulfide > 70 μM near the sediment-water interface. Speciation of sulfur, iron, and manganese in the upper 10 cm of littoral sediments, which are covered by < 20 cm of water, are affected by their permeability and, to a lesser extent, by roots penetration. In the case when sediments are not covered by water, oxygen penetration to the sediments by desiccation is an additional important control of the redox zonation in the surface sediments. In the shore sediments, despite relatively high concentrations of sulfate in the pore-waters, sulfur cycling may be described as "cryptic" as expressed by very low concentrations of hydrogen sulfide in the pore-waters. This is most likely a result of its fast reoxidation by the abundant highly reactive Fe(III) and Mn(IV) phases. Supplementary Information: The online version contains supplementary material available at 10.1007/s10201-024-00756-7.
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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".