Changes of Soil Chemical Properties during Rice Straw Decomposition in Different Types of Acid Sulphate Soils
Bibliographic record
Abstract
The Role of Peat Layers on Iron Dynamics in Peatlands (A Fahmi, B Radjagukguk, BH Purwanto and E Hanudin): The research aimed to study the effect of peat thickness and humification stage of the peat material on Fe solubility at the peatlands with sulfidic material as substratum. The research was conducted at three conditionals of ombrogen peatlands ie ; deep, moderate and shallow peat. Soil samples were collected by using peat borer according to interlayer (the border layer of peat and mineral layer) and conditional of soil horizons. The sample point depth were (cm) G.s2 : 25, G.s1 : 50, Int.s : 70, M.s1 : 90 and M.s2 : 100 for shallow peat, G.m2 : 47, G.m1 : 100, Int.m : 120 and M.m1 : 135 for moderate peat and G.d3 : 50, G.d2 : 150, G.d1 : 200, Int.d : 220 and M.d1 : 235 for deep peat respectively. The results showed that most of Fe on the tested soils was found in organic forms. The peat layers above the sulfidic material decreased the Fe2+ solubility at peatlands. Fe2+ concentration in peat layer decreased with its increasing distance from sulfidic material. There was any other processes beside complexation and chelation of Fe2+ by humic material and its processes was reduction of Fe3+ and this conditions was reflected in redox potential values (Eh).
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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".