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Record W4255380884 · doi:10.1520/stp37689s

Overview of Natural Attenuation of Sediments

2006· book-chapter· en· W4255380884 on OpenAlexaff
CN Mulligan, RN Yong

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldAgricultural and Biological Sciences
TopicSoil erosion and sediment transport
Canadian institutionsConcordia University
Fundersnot available
KeywordsNatural (archaeology)AttenuationGeologyEarth scienceEnvironmental sciencePaleontologyPhysicsOptics

Abstract

fetched live from OpenAlex

Natural attenuation involves the use of the natural processes with the soil and groundwater to remediate contamination by physical, chemical, and biological processes to reduce the risk to human health and the environment. Although the use of natural attenuation as a treatment process is increasing for remediation of contaminated groundwater, much less research has focused on contaminated soils and sediments. Industrial effluents, agricultural runoff, and sewage discharges are major sources of contaminants for the sediments. In addition, benthic organisms can transport contaminants through bioturbation and there is considerable variability at sites. Organic materials, a particularly important component of the sediments, can sequester the contaminants. Sediment-water partitioning controls the release of the contaminants into pore water and benthic organisms. Fate and transport mechanisms for both organic and inorganic contaminants within the sediments need to be understood to establish protocols for the monitoring and use of natural attenuation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0160.011

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 head0.046
GPT teacher head0.239
Teacher spread0.192 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations1
Published2006
Admission routes1
Has abstractyes

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