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Record W4289443700 · doi:10.21203/rs.3.rs-1840336/v1

55 xenobiotic organic compounds in Tripoli Landfill-Lebanon Leachate and their Fluxes to the Abou Ali River and Mediterranean Sea

2022· preprint· en· W4289443700 on OpenAlexfundno aff
Ahmad Moustafa, Mariam Hamzeh, Moamen Baroudi, Baghdad Ouddane, Sopheak Net

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsnot available
FundersAgence Universitaire de la Francophonie
KeywordsLeachateEnvironmental chemistryPollutantEnvironmental sciencePhthalic acidPollutionMediterranean climateExtraction (chemistry)ContaminationMediterranean seaEnvironmental engineeringChemistryEcologyChromatography

Abstract

fetched live from OpenAlex

Abstract Pollution generated from landfill solid wastes constitute one of the major threat to the environment. The landfill leachate contains various toxic pollutants, making it the most dangerous issue of the landfills. Monitoring the xenobiotic organic concentrations in landfill leachate is an important step to evaluate the environmental impacts. This work aims to monitor the concentration of organic pollutants such as polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), phthalic acid esters (PAEs), and bisphenols (BPs) in leachate samples collected from the old and new units of Tripoli landfill-Lebanon. The organic pollutants were extracted by using the solid-phase extraction and quantified by using GC-MS/MS. The results showed high level of PAEs, BPs, PCBs and PAHs in the leachate samples. The fluxes of pollutants to the Abou Ali River and Mediterranean Sea have been detected at 0.23 kg, 0.01 kg, 116.85 kg, 15.93 kg, and 7.58 kg for Σ16PAHs, Σ28PCBs, Σ6PAEs, Σ4BPs, and 4-NP respectively.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.042
GPT teacher head0.329
Teacher spread0.287 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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Same venueResearch Square→Same topicToxic Organic Pollutants Impact→French-language works237,207→