{"id":"W4318578571","doi":"10.32920/21979688.v1","title":"Researchers’ Response to Canada’s Fundamental Science Review","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Canadian Policy and Governance","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Government of Canada","keywords":"Summit; Library science; Political science; Geography; Cartography; Computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.04609496,0.001188982,0.002040442,0.007331084,0.02112082,0.02292523,0.005624008,0.03247141,0.0131787],"category_scores_gemma":[0.1142814,0.001032615,0.002347257,0.01004525,0.007862131,0.004333292,0.006638587,0.02502075,0.003117052],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.1845597,"about_ca_system_score_gemma":0.5089742,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9084172,"about_ca_topic_score_gemma":0.9416565,"domain_scores_codex":[0.9491203,0.004936576,0.001958523,0.002587029,0.03262376,0.008773926],"domain_scores_gemma":[0.8044676,0.03237899,0.003401071,0.004417256,0.1191305,0.03620444],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002435653,0.000005469382,0.0002406972,0.0001317657,0.00002386642,0.00008209439,0.0002772716,0.00006128132,0.00008850923,0.007665164,0.9884831,0.002916338],"study_design_scores_gemma":[0.00002959528,0.000006859117,0.001476306,0.0001924154,0.00002899538,0.00001935084,0.0005005256,0.00009298656,0.0001296796,0.001695442,0.9957888,0.00003910185],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.0008654085,0.009644832,0.0001165223,0.9535346,0.02424321,0.0000648612,0.0009975597,0.00006569659,0.01046726],"genre_scores_gemma":[0.02750076,0.01369635,0.001017433,0.8706023,0.01513941,0.000220655,0.001332829,0.0002067312,0.07028355],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.953905,"threshold_uncertainty_score":0.9457952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2044441968592472,"score_gpt":0.450809056945578,"score_spread":0.2463648600863309,"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."}}