{"id":"W6968393378","doi":"10.5281/zenodo.15285967","title":"SPCG Computational Artifact for SC25","year":2025,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Artifact (error); Feature (linguistics); Noise (video); Set (abstract data type)","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":[0.001063692,0.001499207,0.0009897494,0.002757979,0.001463242,0.002864928,0.003424168,0.001896477,0.1994837],"category_scores_gemma":[0.00949988,0.0005174418,0.001952108,0.003348638,0.0006350641,0.001745551,0.001613461,0.00161609,0.08241342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001593389,"about_ca_system_score_gemma":0.00382887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02167589,"about_ca_topic_score_gemma":0.04454812,"domain_scores_codex":[0.9987774,0.0001419727,0.00006634105,0.0003408573,0.0005133624,0.0001599911],"domain_scores_gemma":[0.9976485,0.0004771467,0.00003816236,0.001012076,0.0007442985,0.00007975807],"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":[0.0003817093,0.0000846933,0.0003340431,0.0002727749,0.00008092103,0.0001780308,0.00003281782,0.02060242,0.001439678,0.04747351,0.7842314,0.144888],"study_design_scores_gemma":[0.0003474329,0.00008919251,0.0008337401,0.0001287726,0.00007157237,0.0003078421,0.0000663551,0.3857077,0.01300258,0.1568404,0.4425362,0.00006818667],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.01748502,0.00124412,0.3997249,0.002874183,0.004877505,0.0004596535,0.1365274,0.1164339,0.3203732],"genre_scores_gemma":[0.1730593,0.0005994529,0.4790968,0.001057522,0.0007932378,0.0008383368,0.1747991,0.02831135,0.1414449],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.1994837,"threshold_uncertainty_score":0.6673396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02387416727287491,"score_gpt":0.2721165814351881,"score_spread":0.2482424141623132,"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."}}