Carbon storage potential and its distributions in the particle size fractions in Harran Plain, Turkey
Bibliographic record
Abstract
In recent years, there has been increasing international interest in increasing and sustainably managing soil C stocks to contribute to combating climate change and support food security. In this context, determining the C storage capacity of soils and examining the distribution of soil C based on fractions is of great importance for a better understanding of C dynamics. The present study investigated the storage potential of soil organic carbon (SOC), inorganic carbon (SIC) and total carbon (TC) in 16 selected profiles, and SOC and SIC distribution in five different particle size fractions (2000-425μm, 425-150 μm, 150-106 μm, 106-75 μm, <75 μm) of the Harran plain in Turkey. The results revealed that the particle size distribution in the surface layer varied in the following order depending on soil weight: 850-250> 2000-850> 250-150> 150-75> 75 μm. The organic C content of the soils is low due to the semi-arid climate conditions. Fraction-based soil SOC distribution was in the following order: 11% at 2000-850 μm, 15% at 850-250 μm, 21% at 250-150 μm, 23% at 150-75 μm and <75 μm 30%. Organic matter fractions differed according to the particle size distribution and the applicable soil management system. Stable organic matter content was significantly related to clay content and greatly influenced by the type of soil management used.
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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 | 0.001 |
| 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".