{"id":"W4386860893","doi":"10.1001/jamanetworkopen.2023.31905","title":"National Institutes of Health Funding Gaps for Principal Investigators","year":2023,"lang":"en","type":"article","venue":"JAMA Network Open","topic":"Health and Medical Research Impacts","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institutes of Health; National Institute for Health and Care Research","keywords":"Quarter (Canadian coin); Fiscal year; Principal (computer security); Political science; Medicine; Business; Geography; Finance","routes":{"ca_aff":false,"ca_fund":false,"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":[0.04120105,0.0004791246,0.0007568432,0.004965533,0.002686467,0.004919235,0.002429796,0.001471879,0.01057608],"category_scores_gemma":[0.1688887,0.0006274161,0.0007302574,0.01197029,0.001433221,0.003973876,0.008459794,0.002395626,0.003734311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004700208,"about_ca_system_score_gemma":0.02517467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01956465,"about_ca_topic_score_gemma":0.01687548,"domain_scores_codex":[0.9529853,0.01493994,0.006012741,0.003550821,0.01710165,0.005409458],"domain_scores_gemma":[0.7865382,0.07129642,0.08236711,0.01457792,0.02771872,0.0175017],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005195726,0.0001810099,0.5905905,0.001066849,0.0001801348,0.0001922772,0.002068527,0.0006478104,0.0002286099,0.008233218,0.282818,0.1132734],"study_design_scores_gemma":[0.0002320124,0.0004042931,0.6630337,0.002857777,0.0001677428,0.001162382,0.00594932,0.001631896,0.0007398886,0.01234102,0.3113713,0.000108574],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5289009,0.01795642,0.01610816,0.1425968,0.003372814,0.00224102,0.1475602,0.002864957,0.1383986],"genre_scores_gemma":[0.8976445,0.005412755,0.0189271,0.01310273,0.001490509,0.002794605,0.04919165,0.0002822373,0.01115389],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9587989,"threshold_uncertainty_score":0.2178946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4036160359107651,"score_gpt":0.5091967631256752,"score_spread":0.1055807272149101,"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."}}