{"id":"W4409078362","doi":"10.26685/urncst.852","title":"Women in Science &amp; Engineering (WISE) Conference: 5MT Competition Abstract Collection","year":2025,"lang":"en","type":"article","venue":"Undergraduate Research in Natural and Clinical Science and Technology (URNCST) Journal","topic":"Gender and Technology in Education","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Competition (biology); Computer science; Data science; Biology; Ecology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004461805,0.001375544,0.001219961,0.002069964,0.005142674,0.008613508,0.001990618,0.002439785,0.4528382],"category_scores_gemma":[0.004660011,0.0005290146,0.001112944,0.001573096,0.000811492,0.001668893,0.006695952,0.002287568,0.2065282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004595875,"about_ca_system_score_gemma":0.007151678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01169752,"about_ca_topic_score_gemma":0.04929662,"domain_scores_codex":[0.9966757,0.000306038,0.0001130388,0.0002947304,0.001962417,0.0006481234],"domain_scores_gemma":[0.9908187,0.0002446828,0.0001844936,0.0002497906,0.003370828,0.005131451],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004311399,0.00002131693,0.00007642271,0.0000653708,0.000001424268,0.0000186175,0.00009176657,0.00001396161,0.0002224148,0.0002757721,0.9906866,0.008483295],"study_design_scores_gemma":[0.00001242619,0.0000473047,0.001387247,0.00007806704,0.000001308561,0.00001332995,0.000267892,0.00002705711,0.0001310981,0.0001576927,0.9978684,0.000008114789],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.01125427,0.009487517,0.002700455,0.07552198,0.1408037,0.002618321,0.02612822,0.002050948,0.7294347],"genre_scores_gemma":[0.01456045,0.002025843,0.001078346,0.003633257,0.01250831,0.001042432,0.01034476,0.0008716324,0.953935],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.4528382,"threshold_uncertainty_score":0.7804599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07206770573257028,"score_gpt":0.4456206232198367,"score_spread":0.3735529174872664,"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."}}