Voyage without constellation: evaluating the performance of three uncalibrated process-oriented models
Bibliographic record
Abstract
Three process-oriented dynamic acidification models were applied to a long-term monitoring site without calibration to evaluate the influence of model structural differences on simulation. The models were simplified to share as many commonalities as possible so that the main structural differences could be investigated. The models differed in sub-models for cation exchange, organic acids and acid anion speciation. All models were populated with ‘equivalent’ parameters by systematic input mapping. The influence of input variability was addressed through Monte Carlo parameter sampling. The three models behaved exactly the same for tracers (e.g. sulphate and chloride), indicating successful cross-parameterization of the models. Differences in model structure had an impact on some of the simulated chemical parameters. In particular, models using Gapon cation exchange simulated higher base saturation levels in the long run than their Gaines-Thomas counterparts, but simulated lower base cation concentration and acid neutralizing capacity in soil solution when acid deposition levels were high. Multiple-model evaluation frameworks as presented here allow for greater certainty in model predictions; ultimately, this type of framework should be employed when evaluating the impacts of future climate and environmental changes on soil and surface water hydrogeochemistry.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".