{"id":"W1169254061","doi":"10.1007/s10336-015-1267-5","title":"A high-accuracy, time-saving method for extracting nest watch data from video recordings","year":2015,"lang":"en","type":"article","venue":"Journal für Ornithologie","topic":"Animal Behavior and Reproduction","field":"Agricultural and Biological Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Thompson Rivers University","funders":"","keywords":"Computer science; Nest (protein structural motif); Intraspecific competition; Software; Data extraction; Focus (optics); Paternal care; Field (mathematics); Real-time computing; Data mining; Ecology; Biology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008069744,0.001067946,0.0008734463,0.002472528,0.0004510773,0.0008230433,0.001479892,0.001099068,0.004787095],"category_scores_gemma":[0.00202782,0.0004422246,0.0005540546,0.001925272,0.0002215382,0.0008027515,0.0008353812,0.0008277257,0.004520696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002238372,"about_ca_system_score_gemma":0.001016759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003703032,"about_ca_topic_score_gemma":0.01214766,"domain_scores_codex":[0.9993923,0.00003828083,0.0000479961,0.0001689195,0.0003102098,0.00004223186],"domain_scores_gemma":[0.9985332,0.000404841,0.0001639653,0.0002102417,0.0006095963,0.00007817181],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003520731,0.0001672101,0.005120018,0.0004907109,0.0001234536,0.0001880006,0.0001570202,0.001448646,0.24838,0.0004272484,0.01148688,0.7316588],"study_design_scores_gemma":[0.00034282,0.0006365781,0.1521604,0.0002407427,0.0006306107,0.003845175,0.0005038176,0.2982793,0.4628609,0.002539519,0.07747537,0.0004848259],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03299705,0.0009272208,0.9480483,0.000123146,0.0003167598,0.0004511632,0.004351375,0.01109131,0.001693774],"genre_scores_gemma":[0.05779474,0.0005209986,0.9324552,0.0001029638,0.0001193719,0.0008558003,0.004492852,0.0003921219,0.003265888],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004787095,"threshold_uncertainty_score":0.0160144,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.202984077151535,"score_gpt":0.3819549495346749,"score_spread":0.1789708723831399,"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."}}