{"id":"W4387453959","doi":"10.1007/s10791-023-09421-6","title":"Learning heterogeneous subgraph representations for team discovery","year":2023,"lang":"en","type":"article","venue":"Information Retrieval","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph; York University; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Overfitting; Ranking (information retrieval); Machine learning; Set (abstract data type); Graph; Task (project management); Artificial intelligence; Baseline (sea); Representation (politics); Data science; Artificial neural network; Theoretical computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002369998,0.0001027899,0.0001011972,0.0002775129,0.0002863486,0.0003397961,0.0003853274,0.00005695781,0.000002189165],"category_scores_gemma":[0.0002747815,0.000100838,0.0001105858,0.001460625,0.00003137578,0.004396531,0.0001381954,0.0001379154,0.0001200105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002482876,"about_ca_system_score_gemma":0.000029264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002065455,"about_ca_topic_score_gemma":6.871988e-7,"domain_scores_codex":[0.9989251,0.00003057215,0.0003233648,0.0001568127,0.0002781244,0.0002860164],"domain_scores_gemma":[0.9990876,0.0002621983,0.000161355,0.0003017118,0.0001248418,0.00006233488],"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":[0.0001528263,0.0000247795,0.002493137,0.00005979114,0.00006092834,0.00000802609,0.003578291,0.8198736,0.0006554859,0.04281274,0.01239689,0.1178835],"study_design_scores_gemma":[0.0008326068,0.0003055158,0.004346172,0.00001906635,0.000007823332,0.00003061024,0.0002125565,0.9289084,0.004234688,0.009526776,0.05121839,0.0003573806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1178128,0.00001496244,0.878769,0.0004765447,0.0007023332,0.0004714478,0.00001356019,0.0009456194,0.0007936924],"genre_scores_gemma":[0.9928805,0.00003795397,0.005806231,0.0003788746,0.00008167373,0.00002464279,0.0001629406,0.000009801276,0.0006173337],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8750677,"threshold_uncertainty_score":0.4112054,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01446712919078797,"score_gpt":0.2738418785845982,"score_spread":0.2593747493938103,"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."}}