Detecting and mitigating ocean tidal loading displacements in the Bay of Fundy using GPS
Bibliographic record
Abstract
Detecting and mitigating ocean tidal loading displacements in the Bay of Fundy using GPS Tidal induced displacement is one of the systematic errors that contribute to the scatter in the geodetic measurements derived from the GPS system. This paper focuses on ascertaining and reducing tidal induced errors due to ocean tide loading (OTL) at two GPS sites, namely CGSJ (Coast Guard Saint John, New Brunswick) and DRHS (Digby High School, Nova Scotia), established under the Princess of Acadia project in Saint John, New Brunswick and in Digby, Nova Scotia, respectively. Baseline solutions were obtained by processing 3 and 24 hourly GPS data for the period of one month, using differential positioning software DIPOP and its client end GUI, FACE v2.0. The observed differential variations of GPS sites were compared to the OTL modeled differential variations along the baselines for statistical analysis. The predictive differential ocean tide loading induced variations of the baselines were modeled with global ocean tide model FES 95.2 (0.5 by 0.5 resolution) supplemented with a higher resolution regional tide model by Pagiatakis (0.25 by 0.25 resolution). After modeling for tidal effects the solutions for both the height component and baseline length show daily repeatability better than 1.5 mm for baselines ranging from 87.5 km to 170 km. The tidal model used in the investigation explains the observed motion considerably well, with correlation coefficients of greater than 0.70 between modeled and observed curves. Elimination of tidal effects has resulted in day-to-day rms reduction of height better than 80 percent.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".