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Water repellency of fly ash‐enriched forest soils from eastern Germany

2010· article· en· W2053008228 on OpenAlexfundno aff
Peter Hartmann, Heiner Fleige, Rainer Horn

Bibliographic record

VenueEuropean Journal of Soil Science · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
FundersBundesministerium für Bildung und ForschungMcGill University
KeywordsFly ashSoil waterTopsoilEnvironmental scienceOrganic matterEnvironmental chemistrySubsoilTotal organic carbonSoil scienceChemistry

Abstract

fetched live from OpenAlex

Fly ash‐enriched soils occur widely throughout the industrial regions of eastern Germany and in other heavily industrialized areas. A limited amount of research has suggested that fly ash enrichment alters the water repellency (WR) characteristics of soil. This study concentrates on the influence of fly ash enrichment on WR of forest soils with a focus on forest floor horizons (FFHs). The soils were a Technosol developed from pure lignite fly ash, FFHs with lignite fly ash, and FFHs without lignite fly ash enrichment. Three different methods (water drop penetration time, WDPT , test; water and ethanol sorptivity measurement and the derived contact angle, θ R ; and the Wilhelmy‐plate method contact angle, θ wpm ) were used to characterize soil WR. Additionally, carbon composition was determined using 13 C‐NMR spectra to interpret the influence of the organic matter. This study showed that the actual WR characteristics of undisturbed, fly ash‐enriched soils can be explained in terms of the composition of soil organic matter, with the fly ash content playing only a minimal role. Regardless of the huge amounts of mainly mineral fly ash enrichment, all undisturbed FFHs were comparable in their WR characteristics and their carbon compositions, which were dominated by recently‐formed organic substances. The pure fly ash deposit was strongly influenced by lignite remains, with the topsoil having a greater content of recent plant residues. Thus, the undisturbed topsoil was more repellent than the subsoil. When homogenized samples were used, we found a distinct effect of fly ash enrichment and structure on WR. Water repellency of the pure fly ash horizons did not differ distinctly, while the fly ash enrichment in the FFHs caused a significant reduction in WR. The methods used ( WDPT , θ R and θ wpm ) identified these differences similarly. These results led to the assumption that water‐repellent structures of the topsoils were probably the result of hydrophobic coatings of recently formed organic substances, whereby the initially high wettability of the mainly mineral, hydrophilic fly ash particles was reduced.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.828
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.007
GPT teacher head0.198
Teacher spread0.191 · 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.

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

Citations7
Published2010
Admission routes1
Has abstractyes

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