GIBSI: an integrated modelling system for watershed management – sample applications and current developments
Bibliographic record
Abstract
Abstract. Hydrological and pollutant fate models have long been developed for research purposes. Today, they find an application in integrated watershed management, as decision support systems (DSS). GIBSI is such a DSS designed to assist stakeholders in watershed management. It includes a watershed database coupled to a GIS and accessible through a user-friendly interface, as well as modelling tools that simulate, on a daily time step, hydrological processes such as evapotranspiration, runoff, soil erosion, agricultural pollutant transport and surface water quality. Therefore, GIBSI can be used to assess a priori the effect of management scenarios (reservoirs, land use, waste water effluents, diffuse sources of pollution that is agricultural pollution) on surface hydrology and water quality. For illustration purposes, this paper presents several management-oriented applications using GIBSI on the 6680 km2 Chaudière River watershed, located near Quebec City (Canada). They include impact assessments of: (i) municipal clean water program; (ii) agricultural nutrient management scenarios; (iii) past and future land use changes, as well as (iv) determination of achievable performance standards of pesticides management practices. Current and future developments of GIBSI are also presented as these will extend current uses of this tool and make it useable and applicable by stakeholders on other watersheds. Finally, the conclusion emphasizes some of the challenges that remain for a better use of DSS in integrated watershed management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".