Can soil properties of Fluvisols be influenced by river flow gradient?
Bibliographic record
Abstract
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 VýÄapy-Opatovce and JelÅ¡ovce 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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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; both teacher heads agree on what is shown here.
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".