Bibliographic record
Abstract
The work presented in this thesis represents a contribution to the area of modeling of the transport and fate of herbicides applied to cropped fields, and was part of a larger research effort geared towards better management of herbicides. The main objective of this thesis was to develop a graphical user interface (GUI) for PESTFADE, a process-based mathematical model of pesticide transport and degradation, and to provide documentation for the execution of PESTFADE. The model simulates changes in pesticide concentration at different depths in the soil, based on relevant physical, chemical, biological and meteorological factors. PESTFADE is considered to be one of the most comprehensive models of its kind. However, it was, until now, difficult to implement due to absence of a user manual and graphical interface suitable for exploitation in a Windows environment. The author developed the GUI in Visual Basic, created macros to facilitate certain calculations, rewrote the original FORTRAN 77 code and then validated the updated version against field data obtained from an experimental site (Eugene Whelan Farm, Woodslea, Ontario). A preliminary development of an artificial neural network (ANN) to perform the same simulation implicitly, with fewer input parameters and less computational time, was also done. The thesis describes PESTFADE and the GUI, gives guidelines for implementing the package, and presents the results of the field validation of the revised version. During this work, the author discovered that there were problems in the parts of the code dealing with sorption phenomena. This can be solved by conventional kinetics or by Gamble kinetics.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".