The Value of Collecting New, Random-Track Bathymetry Data
Bibliographic record
Abstract
Only a small portion of the deep ocean has ever been acoustically measured, and, despite serious efforts to initiate one, there is no program to systematically map the unmeasured areas. Acoustic measuring instruments must operate in contact with sea water, which usually means they are carried by ships. Research ships are not abundant, and access to them is competitive, meaning it is unlikely that the 200 plus ship-years required to multibeam the ocean beyond 500m depths will materialize in the foreseeable future. Research ships are only a small percentage of those afloat; there are approx 90,000 container ships at sea, travelling about 214,000 miles each per year. In shallower waters, commercial fishing fleets traverse an inestimable number of track miles. Can these vessels be used to help fill the gaps in acoustic bathymetry? One surprising development in recent years is the OLEX company's success in getting fishermen to overcome their traditional reticence to tell anyone where they have been, and share echosounder measurements in fishing areas. The shipping industry, thorough its World Ocean Council, is showing willingness to collect data, provided it does not interfere with the vessel's normal operations. Both could be recruited into an ocean users group to map the sea floor, providing: a) An organization existed to assemble the collected measurement, store them in a proper data base and make them available to be used by the scientific community b) The technology existed to enable commercial ships to collect useful depth data without interfering with the crew or cargo. This situation is elaborated in this talk, and the attendees will be asked to participate in a thought - experiment to suggest ways in which this could be done. 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 for 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 representative 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 | 0.028 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".