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Record W2026837593 · doi:10.1100/tsw.2008.98

Pig Manure Application for Remediation of Mine Soils in Murcia Province, SE Spain

2008· article· en· W2026837593 on OpenAlexaff
Ángel Faz, D. Carmona, Andréa Zanuzzi, A. R. Mermut

Bibliographic record

VenueThe Scientific World JOURNAL · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicMine drainage and remediation techniques
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsManureEnvironmental scienceLeachateSoil waterAmendmentAcid mine drainageEnvironmental remediationLimeEnvironmental chemistryContaminationAgronomyChemistryGeologySoil scienceBiologyEcology

Abstract

fetched live from OpenAlex

In southern Spain, specifically in Murcia Province, an increased pig population causes large amounts of slurry production that creates a very serious environmental concern. Our aim was to use this waste to reduce the acid mine drainage process, heavy metal mobilization, and to improve soil conditions to enhance plant establishment in mine soils. Pig manure, sewage sludge, and lime were used as soil amendments in a field experiment and in undisturbed soil column. Field experiments showed an increase in pH, total nitrogen, organic carbon, and carbonate contents; a reduction of diethylene-tetramine pentaacetic acid (DTPA)-- and water-extractable metals; and an improvement of plant establishment. The field studies showed that pig manure could be utilized to remediate polluted soils. Column studies in the laboratory showed that amendment of mine soil with pig manure initially increased soil pH from 2.21 to 6.34, promoted reduced conditions in the surface soil, and decreased the metal mobility. After 21 weeks, while the leachate was slightly acidic, however, the mobility of metals was substantially low. Additions of 7 and 14% of pig manure were insufficient to maintain a neutral pH in the leachate. Therefore, continuous application of the pig manure may be advised.

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)0.002
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.265
Threshold uncertainty score0.253

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.000

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 head0.015
GPT teacher head0.244
Teacher spread0.229 · 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 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".

Quick stats

Citations13
Published2008
Admission routes1
Has abstractyes

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