Project Final Report Optimal Survey Configuration Analysis for Fraser River Bathymetric Mapping By
Bibliographic record
Abstract
This project used GIS as a tool to study the effect of survey configurations on the accuracy of DEM that maps the bathymetry of the Fraser River. The industry sponsor of this project was the Fraser River Project Group at UBC Department of Geography. River bathymetry is a good tool for the study of river morphology changes over time. Two study areas with different river channel morphology were chosen – the Mission reach and Chilliwack reach of the lower Fraser River. Reference bathymetric surfaces were created from a set of densely distributed survey points collected in 1991. For each study area, DEM surfaces were then created using 40 different sets of data points that had different survey configurations (sample pattern, line and point density). In general, two survey configurations were under consideration in this project: cross-sections and diagonals. Linear regression was performed to assess the accuracy of the DEMs relative to the reference bathymetric surfaces that were created with the complete set of data. The results of the statistical analysis suggested that the optimal survey configuration had a transect line spacing of 100 m, and the cross-section survey pattern was superior than the diagonal. This project started on January 3 and ended on May 17; a total of 273 hours of work was devoted to complete this project. ii
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.010 | 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".