{"id":"W1605240210","doi":"","title":"Calibration of an underwater stereoscopic vision system","year":2013,"lang":"en","type":"article","venue":"2013 OCEANS - San Diego","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Underwater; Calibration; Computer vision; Computer science; Artificial intelligence; Stereoscopy; Camera resectioning; Camera auto-calibration; Remote sensing; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001556893,0.0001285323,0.0001688448,0.00009435661,0.00004740579,0.0001871711,0.0005937871,0.00007498881,0.0002108503],"category_scores_gemma":[0.000006496688,0.000096544,0.00004211811,0.0001209743,0.00004543957,0.001640524,0.000115803,0.00007820035,0.00009861312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003543837,"about_ca_system_score_gemma":0.00001795475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001634004,"about_ca_topic_score_gemma":0.00003988447,"domain_scores_codex":[0.9988876,0.00007963877,0.0002943281,0.000264833,0.0002642989,0.0002093054],"domain_scores_gemma":[0.9991568,0.00001954894,0.00008382597,0.000511045,0.0001306082,0.00009820524],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005537392,0.00153462,0.01379738,0.00105583,0.0001564191,0.00001581507,0.00622087,0.00004985009,0.3819293,0.3343209,0.1253292,0.1355345],"study_design_scores_gemma":[0.0008718066,0.003862333,0.01557845,0.0008273254,0.00003257064,0.00001190045,0.0007020113,0.5637732,0.3992852,0.01311273,0.001018445,0.0009240113],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6358721,0.00005029688,0.3534949,0.0003524589,0.0003155459,0.0005518825,0.000002091373,0.0005726881,0.008788042],"genre_scores_gemma":[0.9813785,0.000002915824,0.0182748,0.00007416336,0.0000364716,0.00001448167,0.000003770692,0.000008890859,0.0002059907],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5637234,"threshold_uncertainty_score":0.393695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02468255892477624,"score_gpt":0.2593782568342305,"score_spread":0.2346956979094542,"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."}}