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Record W2903428791 · doi:10.15414/afz.2018.21.02.63-76

Can soil properties of Fluvisols be influenced by river flow gradient?

2018· article· en· W2903428791 on OpenAlexaboutno aff
Vladimí­r Šimanský

Bibliographic record

VenueActa fytotechnica et zootechnica/Acta fytotechnica et zootechnica · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsCambisolEnvironmental scienceSoil horizonSoil scienceSoil classificationSoil waterSoil fertilitySoil morphologyAgronomyBiology

Abstract

fetched live from OpenAlex

Received: 2018-04-19 | Accepted: 2018-05-15 | Available online: 2018-06-30 https://doi.org/10.15414/afz.2018.21.02.63-76 The occurrence of Fluvisols is associated with the rivers, which means that their properties can be greatly influenced by the fluvial activity of the rivers. Therefore, the aim of this work were to (1.) find out whether the flow gradient along the river influenced the soil properties of Fluvisols (2.) evaluate the soil properties of Fluvisols. Soil samples were taken from Nitra River Catchment between villages Výčapy-Opatovce and Jelšovce near Nitra city. There were excavated five soil pits and soils were classified according to the World Reference Base for Soil Resources as follows: Profile 1 as Eutric Fluvisol (Loamic, Humic) (soil use: restored forest), Profile 2 as Eutric Fluvisol (Loamic, Humic) (soil use: arable soil), Profile 3 as Eutric Fluvisol (Loamic, Humic) (soil use: fallow soil), Profile 4 as Eutric Gleyic Fluvisol (Loamic, Humic) (soil use as: forest), Profile 5 as Eutric Fluvisol (Loamic, Humic) (soil use: r aid forest). The investigated Fluvisols had different chemical and physical properties, but not as a consequence of the flow gradient along the river. Differences in chemistry and physical properties of Fluvisols developed along the Nitra River have been significantly affected mainly by its use, soil management practices and depth of the soil profile. Keywords: physical and hydrophysical properties, soil structure, soil sorptive parameters, Fluvisols References ATSIVOR, L. et al. 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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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Open science, Research integrity
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.188
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.004
Science and technology studies0.0010.004
Scholarly communication0.0000.001
Open science0.0060.003
Research integrity0.0030.004
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 head0.019
GPT teacher head0.232
Teacher spread0.212 · 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; both teacher heads agree on what is shown here.

Study designBench or experimental
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".

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Citations11
Published2018
Admission routes1
Has abstractyes

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