{"id":"W2795979476","doi":"10.1145/3173574.3173948","title":"HCI meets Material Science","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":126,"is_retracted":false,"has_abstract":true,"ca_institutions":"Glycemic Index Laboratories","funders":"Engineering and Physical Sciences Research Council; Leverhulme Trust","keywords":"Affordance; Computer science; Human–computer interaction; Context (archaeology)","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":[0.004950844,0.001388507,0.001359166,0.002370022,0.002182471,0.01252616,0.002027449,0.009494592,0.07759616],"category_scores_gemma":[0.01142125,0.0005560176,0.0007152989,0.002125137,0.009656622,0.01668221,0.006679414,0.007755192,0.03413182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002362905,"about_ca_system_score_gemma":0.002920788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001242795,"about_ca_topic_score_gemma":0.00102857,"domain_scores_codex":[0.9941822,0.002204731,0.0003714762,0.0007990572,0.002159104,0.0002835398],"domain_scores_gemma":[0.9911239,0.005101031,0.0002691177,0.001667994,0.001170784,0.0006671466],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005474863,0.00006216697,0.0004076867,0.003604872,0.00005784671,0.0003062324,0.00242424,0.0002618103,0.003323115,0.4050502,0.3024081,0.2820391],"study_design_scores_gemma":[0.000008998213,0.00002685458,0.0002239239,0.0007183829,0.000009703442,0.0004064689,0.0006127783,0.000154384,0.0002346812,0.09283188,0.9047525,0.00001947635],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.00196751,0.2954282,0.03996196,0.23604,0.02049359,0.0002301269,0.0008488548,0.001783459,0.4032463],"genre_scores_gemma":[0.1011501,0.3294634,0.06672543,0.1456101,0.04161048,0.001272823,0.001539949,0.001562688,0.311065],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.07759616,"threshold_uncertainty_score":0.259585,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01116533314271575,"score_gpt":0.2314487099793368,"score_spread":0.2202833768366211,"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."}}