Crowd-Sourced Bathymetry Vertical Uncertainty Calculation per Sounding
Why this work is in the frame
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Bibliographic record
Abstract
Crowd-sourced bathymetry (CSB) data is becoming an increasingly valuable source of information for expanding hydrographic coverage in areas where traditional survey data is limited.However, the quantification of vertical uncertainty in CSB remains a critical challenge, particularly with respect to tidal reduction methods.Standard approaches commonly use nearest-neighbour Voronoi polygons to assign uncertainty from tide stations, but these methods often fail to account for regional tidal variability and the influence of observation period length on prediction uncertainty.This study addresses these issues by evaluating three primary factors: uncertainties in tidal predictions, the influence of tidal observation duration and tidal propagation uncertainty.Observed and predicted tidal elevations for ten representative Canadian tide stations were analyzed using the UTide Python library (Codiga, 2011).Prediction accuracy was assessed via absolute and relative error metrics defined by Collins et al. (2011).To evaluate how observation duration influences uncertainty, tidal predictions were generated using datasets spanning durations from two weeks to one year.RMS prediction errors were calculated for each duration and compared against tidal range across stations.Linear regression was then applied to quantify how prediction error scaled with tidal range for each duration.This approach allowed the estimation of observation-length-dependent uncertainty values ( ) allowing estimation at stations lacking long-term observational records.Figure 1 Vertical uncertainty results associated with tidal prediction across Canadian waters.Colored points represent calculated uncertainties at Integrated Water Level Stations (IWLS), with primary reference stations marked by red stars.Total station uncertainty ( ) was calculated by combining prediction uncertainty ( ), initially calculated as relative error and converted to absolute uncertainty using tidal range R, and the duration-based uncertainty ( ).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it