{"id":"W4410432646","doi":"10.32473/flairs.38.1.138913","title":"Creating Domain-Specific Datasets for Intelligent Environmental Feature Comparison","year":2025,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Advanced Computational Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of Windsor","funders":"","keywords":"Domain (mathematical analysis); Feature (linguistics); Computer science; Artificial intelligence; Data mining; Pattern recognition (psychology); Information retrieval; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001095412,0.0001917349,0.0002159806,0.000127842,0.0006742913,0.0004440063,0.003636539,0.0001077004,0.00002211401],"category_scores_gemma":[0.0001392079,0.0001625329,0.0002469761,0.0007415857,0.0004347525,0.0004705934,0.001362964,0.000531006,0.00001093969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003295514,"about_ca_system_score_gemma":0.0001607252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001109728,"about_ca_topic_score_gemma":0.000002314999,"domain_scores_codex":[0.997427,0.00001803029,0.0005600469,0.0006288398,0.0009619218,0.0004041738],"domain_scores_gemma":[0.9979302,0.0005327617,0.0002400026,0.0003288052,0.0008918,0.00007639679],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002458643,0.0001270958,0.0001389784,0.00002785701,0.00004559653,7.40694e-8,0.0008003162,0.0002302572,0.03228854,0.9268279,0.01339401,0.02609477],"study_design_scores_gemma":[0.00004187101,0.00005248084,0.000099203,0.0001471752,0.000004796098,0.000001840936,0.002591807,0.09444045,0.2509519,0.5582559,0.09325191,0.0001606728],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005900546,0.0001222124,0.9772092,0.01236621,0.0004068751,0.0009094731,0.0001247387,0.00007732661,0.002883452],"genre_scores_gemma":[0.7721577,0.0002459454,0.226286,0.0001975104,0.0001928429,0.0003543994,0.00004441152,0.00001210383,0.0005090891],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7662571,"threshold_uncertainty_score":0.6757655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1059942501670421,"score_gpt":0.4030994945546468,"score_spread":0.2971052443876047,"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."}}