{"id":"W2294147879","doi":"","title":"Blending Science Knowledge and AI Gaming Techniques for Experiential Learning","year":2007,"lang":"en","type":"article","venue":"Summit (Simon Fraser University)","topic":"Augmented Reality Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Beluga Whale; Beluga; Experiential learning; Whale; Rendering (computer graphics); Situated; Citizen science; Computer science; Marine mammal; Human–computer interaction; Fishery; Psychology; Artificial intelligence; Oceanography; Geology","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":[0.002093339,0.000825798,0.0003714621,0.001203144,0.0006029351,0.003950445,0.002074606,0.0009802291,0.008371186],"category_scores_gemma":[0.005563363,0.0003357463,0.0006742169,0.0006772417,0.002262811,0.00324795,0.003769154,0.001133608,0.001381993],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006704706,"about_ca_system_score_gemma":0.0005073212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005693921,"about_ca_topic_score_gemma":0.001224359,"domain_scores_codex":[0.9985706,0.0006867064,0.00007220817,0.0001805835,0.0003837956,0.000106055],"domain_scores_gemma":[0.995667,0.003314994,0.000111556,0.0005560871,0.0001513613,0.0001989861],"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":[0.000235163,0.001121077,0.001773469,0.001544513,0.0000847636,0.0004543068,0.01720298,0.01568856,0.03206158,0.1938404,0.003716873,0.7322763],"study_design_scores_gemma":[0.0002920819,0.001724918,0.00524739,0.000999425,0.0001945484,0.003021204,0.009282477,0.1399921,0.03066587,0.4429535,0.3653916,0.0002348871],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04786857,0.000858643,0.8821638,0.0009689924,0.0001088819,0.0003479501,0.00003483916,0.0008748229,0.06677356],"genre_scores_gemma":[0.3807263,0.001283922,0.6019793,0.0003145167,0.00008805362,0.0007186442,0.00009341757,0.0001653844,0.01463038],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008371186,"threshold_uncertainty_score":0.02800435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01386043443282221,"score_gpt":0.2655097645877405,"score_spread":0.2516493301549183,"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."}}