{"id":"W4311081148","doi":"10.21203/rs.3.rs-2318594/v1","title":"Learning Heterogeneous Subgraph Representations for Team Discovery","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; York University; Toronto Metropolitan University","funders":"","keywords":"Computer science; Overfitting; Ranking (information retrieval); Set (abstract data type); Graph; Machine learning; Task (project management); Artificial intelligence; Baseline (sea); Representation (politics); Data science; Artificial neural network; Theoretical computer science","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":["metaepi_narrow","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001581533,0.0003336756,0.0003935784,0.000797466,0.001403979,0.0009592289,0.002973704,0.000211193,0.00004082719],"category_scores_gemma":[0.000688804,0.0003494134,0.000517429,0.001425253,0.0001895625,0.0005836505,0.007627119,0.003364359,0.00001290177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002836447,"about_ca_system_score_gemma":0.0003398261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009315516,"about_ca_topic_score_gemma":0.00003895663,"domain_scores_codex":[0.9939343,0.001179141,0.0004460412,0.001674602,0.001599668,0.00116625],"domain_scores_gemma":[0.9951295,0.00192539,0.0002020543,0.002041283,0.0004690172,0.0002327154],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001126555,0.0002706275,0.006372299,0.000717719,0.0001682716,0.0001886989,0.001665442,0.9286869,0.000310473,0.02391093,0.00995635,0.0276396],"study_design_scores_gemma":[0.001620905,0.002687003,0.006527884,0.0007212831,0.00004215905,0.00009919592,0.001504953,0.5242821,0.001800552,0.3407873,0.1175115,0.002415149],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06089944,0.003389926,0.921014,0.002340794,0.002168046,0.006106783,0.0002525567,0.001138358,0.002690137],"genre_scores_gemma":[0.9673073,0.000942888,0.02112044,0.00008498243,0.0004887659,0.00418325,0.000462022,0.000113393,0.005296985],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9064078,"threshold_uncertainty_score":0.999896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06686809311051695,"score_gpt":0.410060303289201,"score_spread":0.343192210178684,"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."}}