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Record W2004938198 · doi:10.2166/wst.2008.024

Long-term land application of biosolids–a case study

2008· article· en· W2004938198 on OpenAlexaff
Rao Y. Surampalli, Keith C. K. Lai, Samir K. Banerji, James E. Smith, R. D. Tyagi, B. N. Lohani

Bibliographic record

VenueWater Science & Technology · 2008
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsInstitut National de la Recherche Scientifique
FundersU.S. Environmental Protection Agency
KeywordsBiosolidsGroundwaterEnvironmental scienceLeaching (pedology)Environmental chemistryEnvironmental engineeringNitrateContaminationSoil testSoil waterSoil scienceChemistryEcologyGeology

Abstract

fetched live from OpenAlex

Impact of long-term land application of biosolids on groundwater and soil quality of an application site, which had been operated for 8-15 years, was evaluated in this study. During and after the biosolids application, biosolids-amended soil, groundwater, and background soil samples were collected mainly for pathogen, nitrogen, phosphorus, and heavy metal analyses. Soil test data showed that there was no heavy metal accumulation in the biosolids-amended soil even after 10 years of biosolids application. Similar results were also observed from the groundwater samples in which the heavy metal concentrations in all groundwater samples were well below the maximum contamination levels of the drinking water standards. In addition, bacteriological levels of the soil and groundwater samples were close to the background level and below the permissible limits, respectively, thereby showing no pathogen contamination. However, nitrate-nitrogen contamination of the groundwater was occasionally observed probably due to an excess loading of the biosolids in the past. This problem can be alleviated by applying biosolids at agronomic rates so that no excess nitrogen is available for leaching down to the groundwater.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.682
Threshold uncertainty score0.218

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.228
Teacher spread0.217 · 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

Citations16
Published2008
Admission routes1
Has abstractyes

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