{"id":"W3125757292","doi":"10.3386/w15146","title":"Federal Life Sciences Funding and University R&D","year":2009,"lang":"en","type":"report","venue":"National Bureau of Economic Research","topic":"Innovation Policy and R&D","field":"Economics, Econometrics and Finance","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Office of Science; National Institutes of Health; Ewing Marion Kauffman Foundation","keywords":"Political science; Economics; Gerontology; Medicine","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.006906445,0.0005432654,0.0007968871,0.005602465,0.002341189,0.004357034,0.001337392,0.003478778,0.01319829],"category_scores_gemma":[0.01909046,0.0005215543,0.0008473316,0.004609246,0.0007683193,0.001431034,0.001713511,0.001717788,0.002696638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01114243,"about_ca_system_score_gemma":0.03282745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1667539,"about_ca_topic_score_gemma":0.2595312,"domain_scores_codex":[0.9895755,0.001884767,0.0003897357,0.0005935663,0.004564708,0.002991688],"domain_scores_gemma":[0.9710239,0.01217271,0.003513005,0.001258541,0.008374133,0.003657624],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.003076749,0.001558711,0.290012,0.0006711803,0.0004806078,0.0005899901,0.0007417537,0.003202021,0.00192046,0.1436315,0.4447059,0.1094091],"study_design_scores_gemma":[0.0003582221,0.0004958948,0.6448064,0.000251467,0.0006407656,0.0002627476,0.001349712,0.003209403,0.01194966,0.009088817,0.3274988,0.00008817694],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4192525,0.007313082,0.002254743,0.07594632,0.001506493,0.000559359,0.1006225,0.0006286326,0.3919164],"genre_scores_gemma":[0.7679387,0.003610172,0.001784711,0.006701837,0.0005324641,0.0003522076,0.02851561,0.00009055062,0.1904737],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9930936,"threshold_uncertainty_score":0.3315666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6229066734068825,"score_gpt":0.5032076940676415,"score_spread":0.119698979339241,"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."}}