{"id":"W2952657467","doi":"10.1101/447284","title":"Conducting social network analysis with animal telemetry data: applications and methods using <tt>spatsoc</tt>","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Telemetry; Georeference; Computer science; Relocation; Social network analysis; Social network (sociolinguistics); Data mining; Real-time computing; Telecommunications; Geography; Social media; World Wide Web","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":[0.01152995,0.002144217,0.001653161,0.004072851,0.001152988,0.002959539,0.002268079,0.0007840116,0.04623147],"category_scores_gemma":[0.06496156,0.001291532,0.00322864,0.003197902,0.001213942,0.002207717,0.00331438,0.002664547,0.0222936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008320148,"about_ca_system_score_gemma":0.002674845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005563796,"about_ca_topic_score_gemma":0.007006236,"domain_scores_codex":[0.9917389,0.004021128,0.0008253742,0.001430061,0.001646416,0.0003381218],"domain_scores_gemma":[0.9600185,0.02802071,0.002473405,0.005535058,0.003202273,0.000750083],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006153501,0.0003076268,0.02704771,0.002349726,0.002042403,0.0008603187,0.002546876,0.04716495,0.007838631,0.04205788,0.5299091,0.3372595],"study_design_scores_gemma":[0.0005442844,0.0003148198,0.02443501,0.0007604339,0.0005528425,0.001060811,0.0006398005,0.3990216,0.02388608,0.1318633,0.4164183,0.0005026909],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008358725,0.0002042851,0.8511796,0.000914763,0.0002996189,0.0006880008,0.03758285,0.09669217,0.004080033],"genre_scores_gemma":[0.06106558,0.0002827966,0.8541026,0.0004959359,0.0002002293,0.005033822,0.02958983,0.04398829,0.005240898],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04623147,"threshold_uncertainty_score":0.1546597,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05000030402393389,"score_gpt":0.298067377609319,"score_spread":0.2480670735853851,"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."}}