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Record W4211150132 · doi:10.15832/ankutbd.907173

Carbon storage potential and its distributions in the particle size fractions in Harran Plain, Turkey

2021· article· en· W4211150132 on OpenAlexaff
İbrahim Halil Yanardağ, A. R. Mermut, Ángel Faz Cano

Bibliographic record

VenueTarım Bilimleri Dergisi · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsSoil waterTotal organic carbonSoil carbonContext (archaeology)Particle-size distributionOrganic matterEnvironmental scienceParticle sizeCarbon fibersSoil scienceEnvironmental chemistryChemistryGeographyMathematics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.550
Threshold uncertainty score0.573

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.006
GPT teacher head0.210
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2021
Admission routes1
Has abstractyes

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