{"id":"W4385697923","doi":"10.1007/978-3-031-37731-0_34","title":"Automated Blue Whale Photo-Identification Using Local Feature Matching","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Computer Research Institute of Montréal","funders":"","keywords":"Whale; Scale-invariant feature transform; Artificial intelligence; Computer science; Pattern recognition (psychology); Computer vision; Matching (statistics); Identification (biology); Humpback whale; Feature (linguistics); Feature extraction; Mathematics; Fishery; Statistics; Biology; Ecology","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":[0.0002149213,0.0003684035,0.0004825134,0.001186128,0.0003056107,0.0006035279,0.0006685711,0.0005372656,0.004574744],"category_scores_gemma":[0.0002639128,0.0002161088,0.0004490003,0.001092801,0.0001399032,0.000620015,0.0006812828,0.0002113893,0.004451956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001494087,"about_ca_system_score_gemma":0.0002074787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003465204,"about_ca_topic_score_gemma":0.00561361,"domain_scores_codex":[0.9997935,0.00001591448,0.000006607754,0.0000599807,0.00008545921,0.00003861342],"domain_scores_gemma":[0.9998567,0.0000240329,0.00001119665,0.0000407796,0.00005951213,0.000007827494],"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":[0.0001058027,0.00005632963,0.002261712,0.00009979169,0.00004176799,0.00009407869,0.00005121618,0.002865544,0.1484708,0.0005664508,0.005951548,0.839435],"study_design_scores_gemma":[0.00004282608,0.0004355754,0.08095208,0.0001172681,0.0002270218,0.00236514,0.0005154991,0.5312315,0.3160307,0.004457252,0.06350367,0.0001214473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1348667,0.001893008,0.8361661,0.000130422,0.0002657568,0.0001577563,0.001195368,0.007835839,0.01748903],"genre_scores_gemma":[0.4739507,0.001101643,0.47857,0.0001669653,0.0001000149,0.000140711,0.003928261,0.0004465461,0.04159519],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004574744,"threshold_uncertainty_score":0.01530403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02227995289422053,"score_gpt":0.2559672483046587,"score_spread":0.2336872954104381,"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."}}