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Artificial sweeteners in Ontario streams

2024· dataset· en· W6963083548 on OpenAlexaffabout

Bibliographic record

VenueECCC Data Catalogue · 2024
Typedataset
Languageen
Field
Topic
Canadian institutionsMinistère des Forêts, de la Faune et des ParcsEnvironment and Climate Change Canada
Fundersnot available
KeywordsArtificial SweetenerSTREAMSEffluentGroundwaterSurface waterWastewater

Abstract

fetched live from OpenAlex

To quantify the influence of septic system effluent on streams, 294 samples from 173 stream sites in southern Ontario, Canada, were collected between 2008 and 2015 and analyzed for four artificial sweeteners, acesulfame, saccharin, cyclamate, and sucralose. These artificial sweeteners are powerful tracers of wastewater in the environment due to their widespread use in consumer products and their high concentrations in raw and treated wastewater. Septic systems are known to contribute artificial sweeteners to groundwater, which can subsequently discharge to surface waters. 91% of the stream water samples collected as part of this dataset contained one or more artificial sweeteners, indicating a contribution of groundwater that originated from septic system effluent. Detailed information on the collection and interpretation of this data is published in Journal of Hydrology X 7: 100050. https://doi.org/10.1016/j.hydroa.2020.100050.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.352
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0060.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.354

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 head0.087
GPT teacher head0.308
Teacher spread0.222 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2024
Admission routes2
Has abstractyes

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