{"id":"W4409973579","doi":"10.1145/3698204.3716444","title":"Integrating Eye Tracking, Feature Use, and Emotional Valence: A Multimodal Approach to Evaluating Search Interfaces","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada; University of Regina","keywords":"Computer science; Eye tracking; Emotional valence; Feature (linguistics); Artificial intelligence; Valence (chemistry); Feature extraction; Computer vision; Human–computer interaction; Psychology; Cognition","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":[0.002694261,0.0009500699,0.0005702967,0.002620279,0.0003458778,0.001403861,0.0003883076,0.0007033183,0.001887814],"category_scores_gemma":[0.01295247,0.0002902057,0.0004682671,0.001119883,0.0003898565,0.001310041,0.001185858,0.0005674755,0.0002810007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004441805,"about_ca_system_score_gemma":0.0002509266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009678441,"about_ca_topic_score_gemma":0.001683362,"domain_scores_codex":[0.9980404,0.000817161,0.0001684112,0.0002371165,0.0006238852,0.000113044],"domain_scores_gemma":[0.9927167,0.004075094,0.001172221,0.0003927368,0.00140073,0.0002426705],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.004889038,0.002285819,0.1374436,0.002317648,0.0005204434,0.0002686465,0.006604119,0.003861242,0.5311493,0.001346256,0.001604168,0.3077096],"study_design_scores_gemma":[0.0002864801,0.007284752,0.8674742,0.0002687887,0.000577222,0.0005669835,0.003490386,0.03989876,0.07548925,0.002444367,0.00193371,0.0002850257],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9262983,0.0003589952,0.06568071,0.00009041339,0.00003954312,0.0009827361,0.0005749358,0.0004310318,0.005543406],"genre_scores_gemma":[0.9507792,0.0001875019,0.04616157,0.00009720274,0.00003541499,0.00119493,0.0002824153,0.00005952036,0.001202179],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002694261,"threshold_uncertainty_score":0.01424879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04454853921092032,"score_gpt":0.3822413397368842,"score_spread":0.3376928005259639,"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."}}