{"id":"W2561620318","doi":"10.1002/rob.21698","title":"Robotic Coral Reef Health Assessment Using Automated Image Analysis","year":2016,"lang":"en","type":"article","venue":"Journal of Field Robotics","topic":"Coral and Marine Ecosystems Studies","field":"Environmental Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Australian Centre for Field Robotics","keywords":"Coral reef; Computer science; Artificial intelligence; Support vector machine; Set (abstract data type); Data set; Data mining; Computer vision; Ecology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003724233,0.000107834,0.0003864907,0.00008748408,0.0001148628,0.00002474086,0.0001659415,0.00003901538,0.0004160702],"category_scores_gemma":[0.000061356,0.00006414328,0.0001809162,0.0003278442,0.00003819049,0.0002058631,0.0001711193,0.0001179779,0.00001874527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002967603,"about_ca_system_score_gemma":0.00004205352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000208443,"about_ca_topic_score_gemma":0.0002064479,"domain_scores_codex":[0.9987701,0.00007058061,0.0005106943,0.0001152733,0.0003035957,0.0002298091],"domain_scores_gemma":[0.999096,0.0001051801,0.0004829307,0.0001499501,0.00004003781,0.0001259314],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003524691,0.0002681686,0.8897352,0.0000432256,0.0008378256,0.0001347619,0.0001858835,0.05109372,0.007833299,0.0001616157,0.03678505,0.01288598],"study_design_scores_gemma":[0.0009989152,0.001034174,0.8935677,0.0001715739,0.0007122899,0.0001573686,0.0001663937,0.1012124,0.0001949511,0.0004393875,0.0009824706,0.000362295],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3508106,0.0002097863,0.6108009,0.03311059,0.001774496,0.0002781316,0.00000525188,0.0001142109,0.00289595],"genre_scores_gemma":[0.9569095,0.0001043547,0.04215324,0.0002855021,0.00009142472,3.432425e-7,3.184523e-7,0.000006692633,0.0004486258],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6060989,"threshold_uncertainty_score":0.4555677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02105942305630593,"score_gpt":0.3127273378393416,"score_spread":0.2916679147830357,"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."}}