{"id":"W3101898002","doi":"","title":"3D reconstruction of underwater scenes using acoustic imagery","year":2010,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Artificial intelligence; Computer vision; Computer science; 3D reconstruction; Azimuth; Underwater; Process (computing); Feature extraction; Noise (video); Transformation (genetics); Iterative reconstruction; Feature (linguistics); Orientation (vector space); Enhanced Data Rates for GSM Evolution; Image (mathematics); Geology; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001987619,0.001023955,0.0006065392,0.001114904,0.0002686442,0.001286459,0.0005112019,0.0007336081,0.006340081],"category_scores_gemma":[0.0006919608,0.0009621644,0.0008784546,0.001264196,0.0004382891,0.0008078153,0.001172419,0.001071158,0.002149061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002897567,"about_ca_system_score_gemma":0.0008782905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004233668,"about_ca_topic_score_gemma":0.006145316,"domain_scores_codex":[0.9997732,0.00001563716,0.000007440527,0.00003769566,0.000134596,0.00003156383],"domain_scores_gemma":[0.9998127,0.00003957412,0.000020427,0.00004522498,0.00005467147,0.00002743533],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003996579,0.00012912,0.003253495,0.0004570186,0.0001513592,0.0009282784,0.0004537,0.3165376,0.2841102,0.005206751,0.0112638,0.3771091],"study_design_scores_gemma":[0.00003549892,0.0000570878,0.006353185,0.00005635237,0.00004317342,0.0006001474,0.0002415085,0.9297301,0.04731127,0.003922439,0.01157871,0.00007046673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07581136,0.0004620962,0.9092988,0.0004729219,0.0002193328,0.0001094753,0.001566121,0.003542821,0.00851707],"genre_scores_gemma":[0.4210858,0.001455344,0.5625158,0.0001691268,0.0001473927,0.0001105807,0.002792772,0.0008375726,0.01088557],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006340081,"threshold_uncertainty_score":0.02120972,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0268589196016712,"score_gpt":0.2459069221916855,"score_spread":0.2190480025900143,"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."}}