{"id":"W7134624632","doi":"","title":"Grants, Etc.","year":2009,"lang":"en","type":"other","venue":"Deep Blue (University of Michigan)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Matching (statistics); Information access; Government (linguistics); Access to information; Confidentiality; Information technology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001397139,0.0004295449,0.0007295445,0.0009416171,0.000111679,0.00001064404,0.001036386,0.0006185438,0.008826474],"category_scores_gemma":[0.00001325085,0.0006046989,0.000327211,0.0004970724,0.0003278839,0.0001013115,0.0001603179,0.0004033401,0.01402383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005865479,"about_ca_system_score_gemma":0.00008388926,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001161363,"about_ca_topic_score_gemma":0.2080451,"domain_scores_codex":[0.9982672,0.000107288,0.0001333512,0.000590556,0.0004605432,0.0004410319],"domain_scores_gemma":[0.9983743,0.00002385313,0.0005082695,0.0008365404,0.0000651068,0.0001918603],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002933329,0.0006554184,0.00004134641,0.0003335406,0.001205897,0.0007702382,0.05238971,0.00003431142,0.002224674,0.002029816,0.9290578,0.01096388],"study_design_scores_gemma":[0.001416213,0.00007206055,0.000377798,0.0002854288,0.0003581237,0.0000161466,0.01117837,0.00007380271,0.00004268677,0.0001232788,0.9853923,0.0006637655],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.04475963,0.00222745,0.000946949,0.0001002409,0.0003027958,0.0005359535,0.000975197,0.001018223,0.9491336],"genre_scores_gemma":[0.004261713,0.000373371,0.005595032,0.0001120195,0.0002089157,9.852683e-8,0.0004213971,0.0009603988,0.988067],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.2068837,"threshold_uncertainty_score":0.9996405,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006075169532519041,"score_gpt":0.1755794622224482,"score_spread":0.1695042926899291,"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."}}