All Bathymetry is Predicted Bathymetry: Ponderings and Prognostications After 40 ± 5 at 95% Confidence Years of Being a Bathymetrist
Bibliographic record
Abstract
I have been incredibly fortunate to have begun my adventures before over-the-horizon-positioning, before plate tectonics became accepted as a paradigm, before echo sounders produced more than depth, before computers did much at all…but the future is much more interesting than the past, and in this talk I want to elaborate on some actions I hope are taken in bathymetry in the future. The talk is structured around one word that is mis-used in ocean mapping, namely "predicted". Most bathymetry maps and grids are produced from acoustic measurements. A few in shallow water derive from LIDAR and a few world-scale maps and grids are produced from satellite altimetry. Satellite altimetry is sometimes referred to as "predicted bathymetry ".This terminology confers a certain smug superiority to "acoustic bathymetry", an implied definiteness or certainty that it is more accurate than any mere prediction. However, acoustic bathymetry is predicted, too, and acoustic bathymetrists have little to be smug about. This talk begins with showing the predictions that are used to produce acoustic bathymetry. Having put acoustic bathymetry into the same category as satellite altimetry, it compares the strengths and weaknesses of each. It goes on to show how the two can, and indeed must, be used together when producing deep ocean bathymetry. Some seriuos work need to be done on combining MBES results from different sources, on combining MBES and single beam, on combining both with satellite altimetry- derived bathymetry, and bringing them all together into a common data base. Presenter Bio Dave Monahan is Program Director for the Nippon Foundation General Bathymetric Chart of the Oceans (GEBCO) training program in oceanic bathymetry and Affiliate Professor. Prior to joining CCOM, he served 33 years in the Canadian Hydrographic Service, working his way down from Research Scientist to Director. During that time, he established the bathymetric mapping program and mapped most Canadian waters, built the Fifth Edition of GEBCO, led the development of LIDAR, developed and led the CHS Electronic Chart production program, and was Canadian rep on a number of International committees and boards. He has mentored a few people who became Directors, steered CHS through the conversion to NAD 83 and the introduction of GPS, with the Jet Propulsion Laboratory he performed the first world-scale comparison of satellite and acoustic bathymetric data. He designed an algorithm for contouring bathymetry data from random, widely-spaced tracks, made the over ice spot sounding survey pattern more efficient, conducted field studies and authored papers on sea floor geomorphology all around Canada, in adjacent oceanic basins as well as off Senegal/Gambia, Peru and Guyana. Dave also wrote an International Hydrographic Bureau standard, published over ninety maps and a hundred papers and got Canada to ratify UNCLOS. He also did a lot of management things large and small but has no real memory of them. Before joining the Canadian government he was research assistant to the late Mike Keen at Dalhousie University during the exciting period when the theory of Plate Tectonics was the subject of hearty debate. With degrees in Science, in Arts and in Engineering, he is almost diverse enough to understand how little humankind knows about the ocean. He has been Adjunct Professor in the Department of Geography at Carleton University and continues to hold a similar position in the Department of Geodesy and Geomatics Engineering at the University of New Brunswick.
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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 | 0.016 | 0.135 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.025 | 0.010 |
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".