TICKET-UWM: A coupled kinetic, equilibrium, and transport screening model for metals in lakes
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".