A knowledge-based spatial decision support system (SDSS) for coastal zone oil spill response in Anaktalak Bay, Labrador
Bibliographic record
Abstract
Coordinating a successful oil spill response operation requires fast and reliable data access, expert guidance, and efficient communication of information. In this thesis, the theory, development and implementation of prototype oil spill response Spatial Decision Support System (SDSS) are discussed in detail. In order to aid decision-makers and response personnel during the initial hours of a coastal zone oil spill, this SDSS integrates the analysis, storage and visualization functions of a Geographic Information System (GIS), with the logical reasoning capabilities of an expert system. -- The prototype SDSS is developed through three main phases: (1) creation of the oil spill response expert system, (2) modification of the GIS environment and automation of GIS analysis, and (3) integration of the GIS and the expert system through a graphical user interface (GUI). The functionality of the final system is tested by means of four annotated examples, each representing a different oil spill scenario. Results of the sample scenarios demonstrate the successful transfer of knowledge and data between the GIS, the expert system and the user, and indicate that a SDSS can provide a feasible alternative to the traditional oil spill response decision-making 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".