{"id":"W2975103748","doi":"10.18280/ria.330208","title":"Apple Binocular Visual Identification and Positioning System","year":2019,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Remote Sensing and Land Use","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities","keywords":"Computer vision; Artificial intelligence; Computer science; Preprocessor; Binocular vision; Binocular disparity; Identification (biology); Stereopsis; Subtraction; Machine vision; Positioning system; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002904884,0.0000940492,0.0001246707,0.00007072573,0.0001630248,0.0001306571,0.00008628169,0.0000539772,0.0003268231],"category_scores_gemma":[0.00001454029,0.00008288647,0.00003822255,0.0001882775,0.00003503171,0.000135814,0.000007993701,0.00008751358,0.002379039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005479528,"about_ca_system_score_gemma":0.00001071519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004622181,"about_ca_topic_score_gemma":0.00004881963,"domain_scores_codex":[0.9991194,0.00004697287,0.0002389616,0.0002820875,0.0001203434,0.0001922071],"domain_scores_gemma":[0.9995496,0.00007289272,0.0000716694,0.0001928401,0.00003761207,0.00007533783],"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":[0.000104465,0.00007377558,0.2364363,0.0006536681,0.00006379466,0.00005937144,0.003387441,0.1621282,0.01226193,0.003673569,0.0004531019,0.5807044],"study_design_scores_gemma":[0.00003134467,0.00007225868,0.00928221,0.00009632808,0.00001127466,0.0000789582,0.00210935,0.9771299,0.008436728,0.0001196976,0.002475026,0.0001569359],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9827139,0.0004253377,0.009385478,0.00009904806,0.0004245918,0.0001945889,0.00001167894,0.00007942786,0.00666591],"genre_scores_gemma":[0.9981722,0.00004268723,0.0003269489,0.00003365378,0.0000714249,2.477642e-7,0.00009033217,0.000003971612,0.001258599],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8150017,"threshold_uncertainty_score":0.9983977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01362872090169649,"score_gpt":0.2250691910193072,"score_spread":0.2114404701176107,"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."}}