Identifying community-driven priority questions in acoustic backscatter research
Bibliographic record
Abstract
Introduction Remotely-sensed acoustic backscatter is an indispensable tool for seabed mapping, among other disciplines. Almost a decade after the GeoHab Backscatter Working Group published its guidelines and recommendations report, new technologies, new challenges and new questions have emerged. Given the range of potential backscatter research avenues, it can be difficult to align research programs with the priorities of the community of practice. Prioritization of backscatter research topics is thus necessary to establish a roadmap for acoustic backscatter research efforts. Methods We asked the international community working with acoustic backscatter to submit their priority research questions over a 5- to 10-year horizon. We analyzed and curated a total of 177 research questions from 73 contributors, and the resulting 104 questions were grouped into eight broad recurring themes: “Technologies”, “Calibration”, “Data acquisition and ground-truthing”, “Data processing”, “Post-processing, quality control, data handling, and curation”, “Data analysis”, “Data interpretation”, and “Applications and end uses”. A follow-up survey based on the final list of questions was distributed to characterize the community working with backscatter and to identify key research priorities. Results A total of 120 responses originating from 23 countries were used for the analyses. Most respondents were researchers (68%), while others were technicians (25%) or department or program managers (11%), among other roles. Affiliations of respondents included academia (43%), governmental agencies (37%), and industry/private sector (18%). After scaling the responses, the most commonly selected theme was “Post-processing, quality control, data handling, and curation”, followed by “Calibration” and “Data analysis”. Respondents consistently ranked several research questions as priorities. The two questions that were identified as priorities by over 25% of respondents were “How can we move towards absolute calibration of different systems to allow interregional comparisons?”, and “How can we quantify seafloor backscatter quality and develop standards similar to what exists with bathymetry?”. Discussion All eight themes are represented in the top 10 priority questions, underscoring the need for contributions to backscatter research from multiple perspectives to advance the field. The ranking of priority questions encourages collaboration within the community and will serve as a roadmap for backscatter research programs over the next decade.
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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 | 0.092 | 0.204 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".