{"id":"W4417336740","doi":"10.5194/ica-abs-10-258-2025","title":"Advancing Seafloor Mapping with Machine Learning: A Point Convolution Approach for Filling the Gaps in Multibeam Echo Sounder Data","year":2025,"lang":"en","type":"article","venue":"Abstracts of the ICA","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Point (geometry); Echo sounding; Echo (communications protocol); Convolution (computer science); Seafloor spreading","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001317993,0.0001122809,0.0001503663,0.00009283285,0.0002641944,0.000066759,0.000826976,0.00005220845,0.00003003302],"category_scores_gemma":[0.0003907577,0.00006007091,0.00003254507,0.0002787814,0.0001350055,0.000202761,0.0001086136,0.0004143257,0.000003351445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001615602,"about_ca_system_score_gemma":0.0001367291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003455144,"about_ca_topic_score_gemma":0.003535651,"domain_scores_codex":[0.9987268,0.00009382256,0.000261418,0.000283356,0.0002816271,0.0003530064],"domain_scores_gemma":[0.9985919,0.0006864856,0.000115566,0.0005049431,0.00006172207,0.00003936563],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001251002,0.00004360232,0.04495048,0.000169285,0.00004772723,0.000001407978,0.0003262222,0.9506071,0.00084628,0.000009027403,0.0001173075,0.002756486],"study_design_scores_gemma":[0.0005268684,0.00004502104,0.0528551,0.0001006933,0.00002109153,0.000005611552,0.001141298,0.9425372,0.000791568,0.0006306525,0.001246857,0.00009797791],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4117713,0.001728808,0.5500023,0.005956188,0.0003994081,0.004186518,0.0006482606,0.0001183239,0.02518892],"genre_scores_gemma":[0.9788777,0.00002771749,0.02038322,0.00009217364,0.00002713745,0.000003772556,0.0001851392,0.000004997929,0.0003981064],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5671064,"threshold_uncertainty_score":0.5223167,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02952838488884416,"score_gpt":0.2617986031242134,"score_spread":0.2322702182353692,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}