Bibliographic record
Abstract
The management of natural resources has greatly improved over the past decade. Despite advances in modelling the fate of nutrients or in modelling socio-economic effects of different farm management strategies, tools that integrate the advances made in these fields are still lacking. To develop a tool to overcome this gap, this research focused on the development of a multi-objective decision support system (MODSS) to alleviate phosphorus (P) contamination from agricultural fields and small watersheds. A decision supporting framework was designed to allow technical and public users to run the MODSS. The MODSS consists of the following elements: nonpoint source pollution models, an expert system to analyse the output of a qualitative P model, a scenario creation routine, a routine to estimate percentage and load based P reduction, a cost/benefits routine and a trade off analysis routine. Throughout the development of the MODSS, it was necessary to design a modified P Index for Southern Quebec. During the design process, the risk class 'controlled subsurface drainage' was introduced into the parameter subsurface drainage. The risk class was included due to findings that suggested that subsurface drainage is an important pathway for P loss in Southern Quebec. The modified P Index was coupled with a pre-screening routine to shorten the P Index analysis. The MODSS was applied to the Castor watershed, Quebec, Canada. The analysis showed that contributing distance, modified connectivity and P application rates are most likely probable causes for P movement from the fields in the Castor watershed. Additionally, the analysis showed that if BMP to reduce P loss are adopted the farmers could generate a surplus income.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".