Bibliographic record
Abstract
The main objective of this project was to investigate and model phosphorus (P) transport in soil column studies. A model named HYDRUS-NICA was developed, by coupling a hydrological and transport model (HYDRUS-1D model) with an aqueous chemical model (non-ideal competitive adsorption - NICA), to improve the predictions of P transport in soil and water. The HYDRUS-NICA model was developed by replacing the non-linear empirical (Freundlich and Langmuir) equations of the HYDRUS-1D model with the NICA model equations. The numerical accuracy of the HYDRUS-NICA model was then evaluated by comparing the relative errors produced by the HYDRUS-NICA and HYDRUS-1D models. The results showed that the numerical schemes of the HYDRUS-NICA code are stable. The ability of the NICA model to describe phosphate (PO4) adsorption to soil particles was tested using soils collected from agricultural fields in southern Quebec. The surface charge and PO4 adsorption capacity of these soils were measured. Results were used to estimate the NICA model parameters using a non-linear fitting function. The NICA model accurately described the surface charge of these soils and the PO4 adsorption processes. The HYDRUS-1D model was applied to simulate water flow and PO4 transport in re-constructed soil column experiments. The HYDRUS-1D model was calibrated based on physical and chemical parameters that were estimated from different experiments. Overall, the HYDRUS-1D model successfully simulated the water flow in the columns; however, it overestimated the final adsorbed PO4 concentrations in the soil. The discrepancies in the results suggested that the HYDRUS-1D model could not account for the differences in the soil structure found in the columns, or that the Freundlich isotherm could not adequately describe PO4 adsorption. The HYDRUS-NICA model was calibrated and validated with results from re-packed column experiments. The simulated results were then compared with results obtained by the HYDRUS-1D model. The overall goodness-of-fit for the HYDRUS-1D model simulations was classified as poor. The HYDRUS-NICA model improved significantly the prediction of PO4 transport, with the coefficient of modeling efficiency values being close to unity, and the coefficient of residual mass values being close to zero. The HYDRUS-NICA model can be used as a tool to improve the prediction of PO4 transport at the field scale.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".