Hydrobiologia 422/423: 465–475, 2000. M. Jungwirth, S. Muhar & S. Schmutz (eds), Assessing the Ecological Integrity of Running Waters. © 2000 Kluwer Academic Publishers. Printed in the Netherlands.
Bibliographic record
Abstract
transport, water quality This paper introduces GIBSI, an integrated modelling system prototype designed to assist decision makers in their assessment of various river basin management scenarios in terms of standard water physical and chemical parameters and standards for various uses of the water. GIBSI runs on a personal computer and provides a userfriendly framework to examine the impacts of agricultural, industrial, and municipal management scenarios on water quality and yield. A database (including spatial and attribute data) and physically-based hydrological, soil erosion, agricultural-chemical transport and water quality models comprise the basic components of the system. A geographical information system and a relational database management system are also included for data management and system maintenance. This paper illustrates potential uses of GIBSI by presenting two sample applications applied to a 6680 km2 complex river basin (63.2 % forest, 17.2 % agricultural land, 15.3 % bush, 3.1% urban development and 1.2 % surface water; population: 180 000) located in Québec, Canada: (i) a timber harvest scenario and (ii) a municipal clean water program scenario. Simulation results of the timber harvest scenario showed how clear-cut activities could lead to earlier and larger spring runoff than in the investigated reference state. Results of the municipal clean water scenario revealed that substantial reduction in coliform counts and total
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.081 | 0.048 |
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".