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Duplicate Sampling of Surface Films and Associated Pond Water for Herbicides

2000· article· en· W2069199538 on OpenAlexaff
Don T. Waite, Allan J. Cessna, R. Grover, E. J. Woodsworth

Bibliographic record

VenueJournal of Environmental Quality · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Toxicology and Ecotoxicology
Canadian institutionsUniversity of ReginaAgriculture and Agri-Food CanadaEnvironment and Climate Change Canada
Fundersnot available
KeywordsSurface waterSimazineEnvironmental scienceTrifluralinMCPAConfidence intervalEnvironmental chemistryHydrology (agriculture)ChromatographyMineralogyChemistryPesticideEnvironmental engineeringMathematicsGeologyAgronomyAtrazine

Abstract

fetched live from OpenAlex

Abstract This paper describes the variability of herbicide concentrations in the surface film and subsurface water of small, artificial prairie ponds (dugouts) as determined by comparing duplicate samples. Duplicate surface film samples were collected using a horizontally held glass plate and washed into a collection bottle with dichloromethane. Subsurface water samples were collected by plunging bottles into the pond to a depth of approximately 0.25 m. The samples were collected weekly from two dugouts in 1989 and from one dugout in 1990. Samples were analyzed for 2,4‐D, dicamba, bromoxynil, MCPA, triallate, trifluralin, and diclofop using a gas chromatograph interfaced with a mass selective detector. The average variability of the surface film samples was ±40% of the average of the pair with a confidence interval of 97% ( p = 0.05). The average variability of the subsurface water samples was ±25% of the average of the pair with a confidence interval of 71% ( p = 0.05). Possible reasons for the variability, including the nonhomogeneity of the surface film, are discussed.

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 categoriesInsufficient payload (model declined to judge)
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 score0.639
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0150.000

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 head0.028
GPT teacher head0.287
Teacher spread0.259 · 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.

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

Citations7
Published2000
Admission routes1
Has abstractyes

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