{"id":"W7133449565","doi":"","title":"Managing alignment in open source BM; to which extent does Hirschman help understanding community reactions","year":2022,"lang":"en","type":"article","venue":"ORBi UMONS","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"","keywords":"Set (abstract data type); Context (archaeology); Open source; Variety (cybernetics); Work (physics); Government (linguistics)","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":[0.0006795544,0.0001131133,0.0001372428,0.0000806167,0.0006321989,0.00005421311,0.0005647698,0.00005685336,0.00008079212],"category_scores_gemma":[0.0001013081,0.0001011244,0.00003825815,0.0002474305,0.00005096258,0.000003507205,0.001574216,0.0003117691,0.000005566671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001758027,"about_ca_system_score_gemma":0.00005036213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001516918,"about_ca_topic_score_gemma":0.002603645,"domain_scores_codex":[0.9988413,0.0002900313,0.000185972,0.0002564741,0.0001508905,0.0002753269],"domain_scores_gemma":[0.9993501,0.00004225576,0.00005115404,0.0004430454,0.00001358406,0.00009985155],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009862259,0.003665627,0.05445195,0.0002336595,0.0006325668,0.0001672208,0.01507839,0.01058737,0.6605984,0.008255265,0.1624278,0.08291553],"study_design_scores_gemma":[0.001408755,0.001102569,0.007312319,0.0001057323,0.0000391835,0.00006635725,0.1092219,0.0003445553,0.01381788,0.004694079,0.8611544,0.0007322317],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9493644,0.000173956,0.01506093,0.01949007,0.0005013795,0.0005658906,0.00005099195,0.00006728573,0.01472511],"genre_scores_gemma":[0.9960756,0.0000643473,0.0007423103,0.0006510833,0.00004154101,0.0001301952,0.00006958543,0.00001658503,0.002208734],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6987267,"threshold_uncertainty_score":0.4862427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05541735266559551,"score_gpt":0.3112109381665125,"score_spread":0.255793585500917,"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."}}