{"id":"W6925471933","doi":"10.17605/osf.io/s26yb","title":"IWELL (Inuit well-being and learning from the land)","year":2023,"lang":"en","type":"article","venue":"OSF Preprints (OSF Preprints)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Experiential learning; Wildlife; Social learning; Active learning (machine learning); Learning sciences","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001623526,0.0006248153,0.0005529599,0.001348371,0.005996884,0.003307519,0.001296817,0.0005570183,0.008529745],"category_scores_gemma":[0.002163907,0.0003080895,0.0004338769,0.001623638,0.0009583585,0.001029864,0.004543525,0.001508844,0.001237995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01059923,"about_ca_system_score_gemma":0.01134204,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7205651,"about_ca_topic_score_gemma":0.8632301,"domain_scores_codex":[0.9992577,0.0001311389,0.00001953601,0.00009481554,0.0001574875,0.0003392452],"domain_scores_gemma":[0.9971486,0.0001372764,0.0001157388,0.00009951325,0.0007274371,0.001771351],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0006683692,0.003446284,0.3279357,0.001120078,0.0001419291,0.001122047,0.1570951,0.0002886322,0.002456633,0.0143174,0.1406101,0.3507978],"study_design_scores_gemma":[0.00007199105,0.0004730475,0.7663094,0.0005513363,0.0001254988,0.0002183945,0.06816801,0.0002571316,0.001151199,0.001326105,0.1612749,0.00007303782],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8786456,0.002006799,0.0008066753,0.005764441,0.0003033559,0.001620089,0.01748007,0.0001941103,0.09317894],"genre_scores_gemma":[0.9088712,0.001843467,0.004832524,0.001068088,0.00008055561,0.00324919,0.01104301,0.0001249613,0.06888692],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2794349,"threshold_uncertainty_score":0.562161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0133557674602253,"score_gpt":0.2349358195084367,"score_spread":0.2215800520482114,"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."}}