Canadian Bathymetric Gap Analysis and the Comparison of Barometric Pressure Enhanced Predicted Tides to Ellipsoid Referenced Hydrographic Surveys
Bibliographic record
Abstract
Highly accurate bathymetric models are paramount for building an understanding within many research fields including climate change, tsunamis and wave propagation, marine and benthic habitat management and marine navigation studies among others. Developing highly accurate bathymetric models begins with nations understanding their bathymetric holdings and identifying where gaps exist which will assist survey planning and policy making. This study investigates the extent of bathymetric coverage within Canada’s Exclusive Economic Zone; additionally, to reduce the uncertainty in hydrographic surveys, this study investigates the influence of the inclusion ERA5 mean sea level pressure (barometric pressure) on harmonic analysis predicted tide for the vertical reduction of hydrographic surveys, comparing the results to ellipsoid referenced surveys to quantify the uncertainty. Using the Canadian Hydrographic Service’s digital bathymetric data, we show that the bathymetry within Canada’s exclusive economic zone is largely unknown with results indicating 84.89% (4,991,873 km2) are void of soundings and 13.45% (790,690 km2) have full bottom coverage. Implications for Lakebed 2030, which is concerned with the Great Lakes region, show 74.31% (67,482 km2) are void of soundings and 14.16% (12,863 km2) have full bottom coverage. At a hydrographic surveying rate of 1.5% increase in seafloor coverage per year (74,873 km2), Canada can achieve full bottom coverage within their exclusive economic zone by 2079; however, seafloor coverage acquired during Ocean’s Protection Plan increased on average of 22,380 km2 per year. Taking this rate of seafloor coverage change, Canada will obtain full seafloor coverage of their Exclusive Economic Zone by 2246. Furthermore, we show that applying barometric pressure data to predicted tide time-series has the potential to improve the vertical reduction and lower the uncertainty. This is especially significant (p < 0.01) in < 2 meter tidal areas. We show that in the Canadian Arctic where Global Navigation Satellite Systems (GNSS) are susceptible to signal drop out or degradation, the addition of barometric pressure to predicted tide will provide a slightly improved result then using predicted tide only; however, the results seem to be dependent on the geography of the area of study. This study developed a baseline for future seafloor coverage progress comparison, called the Canadian Bathymetric Gap Analysis epoch 2023 (CBGAe2023) and investigated a means to reduce the uncertainty in the vertical component of hydrographic survey solutions, thus increasing the safety to marine navigation.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.002 | 0.000 |
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".