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Record W2991975545

Distribution of polychlorinated biphenyls in agricultural soils from NCR, Delhi, India

2011· article· en· W2991975545 on OpenAlexaboutno aff
Bhup, er Kumar, Sanjay Kumar, Gargi Goel, Richa Gaur, Meenu Mishra, Sanjay Kumar Singh, Dev Prakash, Paromita Chakraborty, Chander Shekhar Sharma

Bibliographic record

VenueAnnals of biological research · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsnot available
Fundersnot available
KeywordsSoil waterEnvironmental chemistryContaminationDry weightChemistryAnimal scienceEnvironmental scienceAgronomyBiologyEcology
DOInot available

Abstract

fetched live from OpenAlex

Twenty eight polychlorinated biphenyls (PCBs) congeners including twelve dioxin-like PCBs were measured in agricultural soils. ΣPCBs ranged between <0.01 – 99.40 ng g-1 (dry wt.) with the mean of 13.44±0.06 ng g-1 (dry wt.). The concentration of DL-PCBs ranged between 0.37- 19.09 ng g-1 (dry wt.) with an average of 6.26±0.03 ng g-1 (dry wt.). PCB-105 (25%), PCB-114 (18%) and PCB-118 (18%), were the dominant congeners. Ortho PCBs accounted for 61% and, non ortho PCBs contributed only 18% to the total DL- PCBs. The toxicity equivalent calculated using WHO 2005-TEFs range from 0.01 to 105.40 pg WHO 2005-TEQ g-1 (dry wt.) with the mean of 13.78±0.11 pg WHO 2005-TEQ g-1 (dry wt.). PCBs contamination in soils from Delhi region was lower than Canadian guideline values. The contamination source of PCBs in soils possibly comes from open waste burning, electronic waste recycling and depositions from industrial emissions.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.242
GPT teacher head0.379
Teacher spread0.137 · 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

Citations9
Published2011
Admission routes1
Has abstractyes

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