Environmental software systems : environmental information and decision support : IFIP TC5 WG5.11 3rd International Symposium on Environmental Software Systems (ISESS'99), August 30-September 2, 1999, Dunedin, New Zealand
Bibliographic record
Abstract
Part I: Enviromatics Introduction. Enviromatics: Environmental Information and Environmental Decision Support R. Denzer. Part II: Environmental Issues. Will 'environmental' be replaced by 'extrasensory'?L. Morawska. Some Current Issues in Using Diffuse Large Datasets for Environmental Modelling in New Zealand G. Mc Bride, et al. Part III: Environmental Information Systems Tools and Techniques. Self-Organising Maps for the Classification and Diagnosis of River Quality from Biological and Environmental Data W. Walley, et al. Case Libraries and Information Theoretic Case Matching for Soil and Water Resources Management S. Dorner, et al. A Distributed Architecture for Environmental Information Systems M. Purvis, et al. Predicting Patterns in Spatial Ecology Using Neural Networks: Modelling Colonisation of New Zealand Fur Seals C. Bradshaw, et al. Patterns of Use of Computer Support for Environmental Accreditation in Rural New Zealand S. Mann, et al. B-Spline Surface Modelling with Adaptive de Boor Grids in Hydroinformatics C. Lichy, et al. What Would a Reusable Meteorology Component for Environmental Models Look Like? C. Maul. The Use of UML for Model Design and Scientific Software Development C. Maul. Part IV: Environmental Information Systems Implementations. Integration of Remote Data Into Water Resources Simulation Software: Now or Never? R. Argent. An EIS Called WuNDa R. Guttler, et al. A Computer-Based Emission Inventory G. Schimak, et al. Soil Quality Indicators on 5he World Wide Web L. Lilburne, et al. BUBI: An interactive Water Utility Benchmarking Website A. Jolma, et al. Teaching EIS Development - The EU Canada Curriculum on Environmental Informatics D.Swayne, et al. Broad-Scale Land Condition Monitoring using Landsat TM and DEM-Derived Data F. Evans, et al. Part V: Environmental Decision Support Systems. WWW Technology based Hydrological Information and Decision Support System V. Keskisarja, et al. Lessons from an Environmental Information System Developed to Select a Radioactive Waste Disposal Site S. Veitch. Water Quality Model Integration in a Decision Support System L. Leon, et al. Integrated Assessments of River Health using Dcision Support Software W. Young, et al. Assessment of Ecological Responses to Environmental Flow Regimes using a Decision Support System Framework W. Booty, et al. Which Buttons and Bars? An Exercise in Community Participation in Decision Support Software Development S. Cuddy, et al. Integration of Environmental Management into Production Organization and Information Systems R. Pillep, et al. A Decision Support System for Real-Time Management of Water Quality in the San Joaquin River, California N. Quinn. Part VI: Special Topics. Environmental Software Systems in Water Resources: Problems and Approaches Workshop Report R. Argent. Environmental Decision Support Systems: Exactly What Are They? Workshop Report D. Swayne, et al.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.103 | 0.071 |
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".