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Record W2155840322 · doi:10.1139/a02-008

Exploitation of composting management for either reclamation of organic wastes or solid-phase treatment of contaminated environmental matrices

2002· article· en· W2155840322 on OpenAlexvenueno aff
Giovanni Vallini, Simona Di Gregorio, A. Pèra

Bibliographic record

VenueEnvironmental Reviews · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial bioremediation and biosurfactants
Canadian institutionsnot available
Fundersnot available
KeywordsBioremediationLand reclamationCompostEnvironmental scienceWaste managementGreen wasteHazardous wastePollutantEnvironmental chemistryBiodegradable wasteContaminationMechanical biological treatmentMunicipal solid wasteChemistryEcology

Abstract

fetched live from OpenAlex

This paper is an overview of the potential use of composting technology in programmes aimed at organic waste recycling (product-oriented perspective) or decomposition of hazardous materials (process-oriented perspective). This latter approach includes composting as a tool for bioremediation of environmental matrices, such as contaminated soils and sediments. In all above-mentioned cases, biological reactions that characterize composting must be managed carefully to allow putrescible residues to become a humified agricultural fertilizer with no phytotoxic effects, or the degradation of organic pollutants (e.g., aliphatic and aromatic hydrocarbons or halogenated solvents) to proceed to the highest extent with formation of innocuous end products. Thus, the fundamental aspects of controlling the microbial environment in composting matrices are singled out as a means for better evaluating the range of adverse conditions possibly responsible for hindering the correct evolution of the process within different applications. Key words: biopiles, bioremediation, composting, ex situ soil biotreatments, in-vessel systems, open systems, organo-nitro explosives, organic waste reclamation, polycyclic aromatic hydrocarbons, soil composting windrows.

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 categoriesInsufficient payload (model declined to judge)
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 score0.127
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.039
GPT teacher head0.290
Teacher spread0.251 · 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 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

Citations10
Published2002
Admission routes1
Has abstractyes

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