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Fuzzy Rule-Based Models to Predict the Partition Coefficients of Nickel and Zinc in Aquifer Materials

2010· article· en· W2137942384 on OpenAlexafffund
D. G. M. Senevirathna, Gopal Achari, Fraser King

Bibliographic record

VenueJournal of Hazardous Toxic and Radioactive Waste · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsNova Chemicals (Canada)Calgary Laboratory ServicesUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsZincNickelFuzzy logicPartition (number theory)Soil waterPartition coefficientMathematicsApplied mathematicsComputer scienceSoil scienceEnvironmental scienceChemistryMaterials scienceMetallurgyArtificial intelligenceChromatography

Abstract

fetched live from OpenAlex

This paper presents two rule-based models based on fuzzy set theory, which can be used to approximately predict the adsorption of nickel and zinc in soils. The models consider organic matter content, cation exchange capacity, and pH as the input parameters and provide the partition coefficients of nickel and zinc. The rule-based models were developed using data available in literature. The models were validated using data obtained from laboratory experiments conducted using six different soil types. The calibrated models were then used to generate plots that can be used to obtain approximate partition coefficients for nickel and zinc.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.198
Threshold uncertainty score0.345

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.227
Teacher spread0.216 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2010
Admission routes2
Has abstractyes

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