Geographic Information System-based tools in environmental management
Bibliographic record
Abstract
The authors reviewed existing modelling platforms as part of a large study of water and pollution pathways through catchments in Ireland (Irish EPA Pathways project). Worldwide, work on producing catchment management tools (CMTs) has been underway for some time and some of the tools identified here date from as early as 1989. Some of the management problems and model conceptualisations have not changed very much but now there is a stronger emphasis on water quality and more concern about a wider range of contaminants. What has changed substantially is the use of Geographical Information System and Graphical Windows interfaces as technologies supporting a wider practical use of these tools. This review of existing CMTs identified three systems which would be candidates if a CMT had to be deployed immediately in Ireland. All have a rigid catchment model structure and lack the flexibility to include any new scientific information or flow-path conceptualisation that may emerge. The same modelling structure is used for all parts of the catchment, with spatial variation represented by parameter variation only and not variation in model structure. They also have rigid graphical user interfaces which cannot be tailored to match any specific requirements that may emerge from the pathways end-user workshops. Thus a CMT with a more flexible and accessible modelling structure is required if the results of current research are to be incorporated.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.021 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".