{"id":"W2395701964","doi":"10.5860/crln.72.11.8675","title":"Introducing CLIPP: College Library Information on Policy and Practice","year":2011,"lang":"en","type":"article","venue":"College & Research Libraries News","topic":"Library Science and Information Literacy","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Smiths Detection (Canada)","funders":"","keywords":"Library science; Computer science; Political science","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.005487187,0.0006511851,0.0005282328,0.004128304,0.002493509,0.01315574,0.001749269,0.004564813,0.2640558],"category_scores_gemma":[0.03144803,0.0006564554,0.0005350681,0.005130639,0.00166439,0.01166846,0.007682299,0.00560177,0.0644983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004088262,"about_ca_system_score_gemma":0.01128112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007785154,"about_ca_topic_score_gemma":0.03126179,"domain_scores_codex":[0.9969059,0.0007429575,0.0001432008,0.0001349155,0.001732947,0.0003401235],"domain_scores_gemma":[0.9725661,0.01466641,0.0008934739,0.002634845,0.003743609,0.005495642],"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":[0.000009027573,0.00003409437,0.0001029944,0.0001166506,0.00000153765,0.00003294222,0.000225574,0.00003729181,0.00006174017,0.005490887,0.9404186,0.05346868],"study_design_scores_gemma":[0.00001200297,0.000007096623,0.0003310771,0.0001736693,0.000002509131,0.00001893924,0.000279643,0.0001103985,0.0001151479,0.004387827,0.994551,0.00001072204],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.001982911,0.002703848,0.01551502,0.1785696,0.01257307,0.0006466666,0.007290228,0.009702175,0.7710164],"genre_scores_gemma":[0.02763432,0.008122585,0.06134848,0.07361516,0.02363778,0.002255687,0.009827991,0.008103281,0.7854548],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9868442,"threshold_uncertainty_score":0.8833545,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08057974590933747,"score_gpt":0.366073902494925,"score_spread":0.2854941565855875,"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."}}