A Hexagonal Framework to Assess and Visualize Bathymetric Data Coverage
Bibliographic record
Abstract
Geospatial inventories and identification of gaps in bathymetric data coverage often rely primarily on the absence or presence of data—if depth measurements exist in a particular location, that area is classified as “mapped”. These assessments typically analyze coverage at a fixed resolution, without considering how characteristics such as depth, density or recency of the source data may change across spatial scales. Consequently, these methods have the potential to misrepresent coverage. Additionally, when applied to the Great Lakes, analysis is usually segregated between Canada and the United States. To help address these shortcomings, the Great Lakes Observing System (GLOS) has developed an innovative method to assess bi-national bathymetric data coverage in the Great Lakes based on hierarchical hexagonal spatial indexing. This approach considers variables such as water depth and data density, resulting in an accurate spatial depiction of bathymetric coverage extensible across multiple spatial scales. This approach is particularly relevant across large areas where the characteristics of data can vary significantly due to different acquisition methods, survey dates and bottom morphologies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".