Implementation of Cyberinfrastructure and Multiple Technology Platforms for Water Resources Management: The North Slope Decision Support System
Bibliographic record
Abstract
This paper will describe the technological aspects of the North Slope Decision Support System (NSDSS) a project currently underway to develop a water resources management solution in support of oil and gas exploration on the North Slope. Sponsored by the Department of Energy, the NSDSS will consist of an information system, software tools for decisions support, and methodologies for facilitating stakeholder involvement in the decision making process. The NSDSS is initially focused on the process of constructing ice roads across the tundra using water in North Slope lakes. Envisioned as a framework for general water resources planning on the North Slope, the NSDSS will not only apply to the water management issues considered here, but will also be applicable to broader environmental management issues and industry development applications. The NSDSS consists of (1) a cyberinfrastructure (CI) composed of a network of federated databases, and (2) a MS Silverlight based web portal tool (NSDSS.net) that allows for easy data exploration, publishing, water quality and quantity analysis, and ice road planning. The CI contains databases of GIS, field observation time series, net-CDF file based General Circulation model results, and user-created models that work with these input data. Using NSDSS.net, users can explore and publish data, create models of water quality and quantity, and assess the impact of proposed ice road alignments in terms of important stakeholder criteria. Among the innovations necessary to implement these features have been methods for serving data from multiple databases in a unified system. This requires 1) semantic mediation to allow "natural language" queries of federated databases, 2) coincident handling of point and grid datasets, 3) unit mediation to convert raw data from its base units to common units for analysis, 4) automated time series processing to ensure time series are converted to the correct interval and statistic from their database source, and 5) ensuring data security in a shared technology framework. Additional innovations include 1) user friendly and power data exploration and publishing tools, 2) a model database, to which users can publish their models for review and re-use, and 3) new web services for checking the acceptability of targeted ice road routes in terms of their likelihood of disturbing endangered species such as polar bears during their denning process.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".