{"id":"W4387869178","doi":"10.7554/elife.91243.1.sa3","title":"eLife Assessment: Integrating Gaze, image analysis, and body tracking: Foothold selection during locomotion","year":2023,"lang":"en","type":"peer-review","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Terrain; Gaze; Context (archaeology); Computer science; Stability (learning theory); Artificial intelligence; Eye tracking; Natural (archaeology); Photogrammetry; Computer vision; Selection (genetic algorithm); Human–computer interaction; Machine learning; Geography; Cartography","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002462106,0.0004531363,0.0005455207,0.003092251,0.000354653,0.001381379,0.0006185887,0.000556846,0.005391331],"category_scores_gemma":[0.008461023,0.0002571155,0.0001860091,0.001154982,0.0002791614,0.001180332,0.000925287,0.0002999064,0.002560848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004468441,"about_ca_system_score_gemma":0.0009422213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009616479,"about_ca_topic_score_gemma":0.02950139,"domain_scores_codex":[0.9990891,0.0001918513,0.00005386947,0.0001744713,0.0004200032,0.00007073009],"domain_scores_gemma":[0.9973121,0.0005622112,0.0003569883,0.0001537976,0.001427955,0.0001869685],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002250417,0.0001003842,0.067375,0.0002261365,0.000101181,0.0001755693,0.0003182553,0.002337059,0.03514468,0.000395681,0.006661341,0.8869396],"study_design_scores_gemma":[0.0001044512,0.0005880542,0.738299,0.0003489618,0.0002539843,0.001273413,0.001212676,0.1488489,0.0667933,0.003396918,0.0386686,0.0002118089],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.6166328,0.004706565,0.3396972,0.001703043,0.0002774614,0.0008661934,0.002510908,0.006323318,0.02728251],"genre_scores_gemma":[0.7975681,0.001916129,0.1765439,0.0002059513,0.0001328529,0.000246065,0.001576204,0.0005229968,0.02128774],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9975379,"threshold_uncertainty_score":0.01912105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03707709950266415,"score_gpt":0.3717458273157415,"score_spread":0.3346687278130774,"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."}}