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Record W1984518605 · doi:10.1002/etc.518

TICKET-UWM: A coupled kinetic, equilibrium, and transport screening model for metals in lakes

2011· article· en· W1984518605 on OpenAlexaboutno aff
Kevin J. Farley, Richard F. Carbonaro, Christopher J. Fanelli, Robert Costanzo, Kevin J. Rader, Dominic M. Di Toro

Bibliographic record

VenueEnvironmental Toxicology and Chemistry · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicMine drainage and remediation techniques
Canadian institutionsnot available
FundersNational Institute of Environmental Health Sciences
KeywordsEnvironmental chemistryMetalDissolutionBiogeochemical cycleChemistrySorptionDissolved organic carbonEnvironmental scienceAdsorption

Abstract

fetched live from OpenAlex

The tableau input coupled kinetic equilibrium transport-unit world model (TICKET-UWM) has been developed as a screening model for assessing potential environmental risks associated with the release of metals into lakes. The model is based on a fully implicit, one-step solution algorithm that allows for simultaneous consideration of dissolved and particulate phase transport; metal complexation to organic matter and inorganic ligands; precipitation of metal hydroxides, carbonates, and sulfides; competitive interactions of metals and major cations with biotic ligands; a simplified description of biogeochemical cycling of organic carbon and sulfur; and dissolution kinetics for metal powders, massives, and other solid forms. Application of TICKET-UWM to a generalized lake in the Sudbury area of the Canadian Shield is presented to demonstrate the overall cycling of metals in lakes and the nonlinear effects of chemical speciation on metal responses. In addition, the model is used to calculate critical loads for metals, with acute toxicity of Daphnia magna as the final endpoint. Model results show that the critical loads for Cu, Ni, Pb, and Zn varied from 2.5 to 39.0 g metal/m(2) -year and were found to be one or more orders of magnitude higher than comparable loads for pesticides (lindane, 4,4'-DDT) and several polyaromatic hydrocarbon (PAH) compounds. In sensitivity calculations, critical metal-loading rates were found to vary significantly as a function of the hydraulic detention time, water hardness, and metal dissolution kinetic rates.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.125
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.018
GPT teacher head0.220
Teacher spread0.202 · 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

Citations19
Published2011
Admission routes1
Has abstractyes

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