{"id":"W2988041151","doi":"10.1016/j.dib.2019.104752","title":"Datasets for face and object detection in fisheye images","year":2019,"lang":"en","type":"article","venue":"Data in Brief","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Face (sociological concept); Computer vision; Artificial intelligence; Object (grammar); Research article; Pattern recognition (psychology); Information retrieval; Cartography; Geography; Library science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006862538,0.001305741,0.000809103,0.002457123,0.0006601281,0.000595612,0.001733299,0.001258551,0.01022826],"category_scores_gemma":[0.001674638,0.0004080468,0.001089088,0.002079432,0.0003914739,0.0008301312,0.001215125,0.001113504,0.01178935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007273649,"about_ca_system_score_gemma":0.0008488779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01874943,"about_ca_topic_score_gemma":0.04333401,"domain_scores_codex":[0.9990339,0.00008768972,0.00008667562,0.0003144782,0.0003541516,0.0001230621],"domain_scores_gemma":[0.9988251,0.0001371451,0.00009666145,0.0003849891,0.0004664168,0.00008958414],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006527611,0.0008649008,0.01815257,0.001694196,0.0002899948,0.0004145182,0.0001903833,0.01099849,0.03330592,0.001899814,0.6411707,0.2903657],"study_design_scores_gemma":[0.0002712951,0.0007583713,0.2784904,0.0006666305,0.0002370698,0.003843809,0.0009214118,0.06618314,0.05928973,0.005597613,0.583283,0.0004574781],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.06716941,0.002599555,0.05739455,0.0005980838,0.0004135577,0.001282893,0.8358704,0.01534643,0.01932509],"genre_scores_gemma":[0.03469509,0.0005464516,0.04403923,0.0002174394,0.00005879916,0.0009356829,0.914569,0.0003400768,0.004598162],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01874943,"threshold_uncertainty_score":0.03728062,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0220758321948565,"score_gpt":0.2681355168964643,"score_spread":0.2460596847016078,"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."}}