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Record W348165990

Distribution of dioxins and furans in size-fractionated suspended solids in Canagagigue Creek, Elmira, Ontario.

2000· article· en· W348165990 on OpenAlexaboutno aff
Mark Stone, M. Haight

Bibliographic record

VenueIAHS-AISH publication · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsSedimentElutriationSuspended solidsEnvironmental scienceEnvironmental chemistryHydrology (agriculture)Suspended loadChemistryEnvironmental engineeringGeologySediment transport
DOInot available

Abstract

fetched live from OpenAlex

A study was conducted to examine the occurrence and concentration of dioxins and furans in separated size fractions of suspended sediment collected in Canagagigue Creek near Elmira, Ontario. Field-based water elutriation systems were used to collect a time-integrated sample of suspended solids from the creek during a spring storm event. The elutriation system hydraulically sorts suspended solids into five predetermined grain-size classes ( 63 μm). Dioxin and furan compounds were detected in suspended sediment fractions at levels below the Ontario Ministry of the Environment and Energy (OMEE) Interim Sediment Quality Guidelines. Average concentrations of the most toxic compounds, 2,3,7,8 T 4 CDD and 2,3,7,8 T 4 CDF, were 20 ppt and 22 ppt, respectively. The data show no trend between grain size and pollutant concentration. Concentrations of dioxins and furans in suspended sediments are consistent with contaminant levels observed in river-bottom and downstream flood-plain sediments. Independent studies show that elevated levels of dioxins and furans in creek sediments accumulate in the tissues of aquatic organisms and may biomagnify through the food chain.

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
Bibliometrics0.0010.001
Science and technology studies0.0020.001
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 head0.006
GPT teacher head0.200
Teacher spread0.195 · 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

Citations1
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueIAHS-AISH publicationSame topicSoil and Water Nutrient DynamicsFrench-language works237,207