{"id":"W2681029348","doi":"","title":"Toronto startup drags NY libraries into the future","year":2011,"lang":"en","type":"article","venue":"","topic":"Library Science and Administration","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Business","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002467313,0.00005251732,0.0000449835,0.000006693707,0.0007299492,0.0002175756,0.0003922555,0.0000483309,0.007125111],"category_scores_gemma":[0.00001746906,0.0000301108,0.00003071083,0.0001198152,0.0003104376,0.003621268,0.0000342919,0.00004157316,0.00009070558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001574047,"about_ca_system_score_gemma":0.0002477829,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.009879024,"about_ca_topic_score_gemma":0.05660931,"domain_scores_codex":[0.9993271,0.00007203533,0.00008588732,0.0001203979,0.0002234506,0.0001710769],"domain_scores_gemma":[0.9996979,0.00003098214,0.00002826913,0.0001406799,0.00001769267,0.0000845091],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000004495804,0.00001391649,0.00324105,6.986206e-7,0.000002156741,8.557461e-7,0.1633289,1.734046e-8,0.0000162346,0.8103552,0.01381299,0.009223499],"study_design_scores_gemma":[0.00003444676,0.00004988636,0.008344224,0.00000155189,0.000002356039,1.461516e-7,0.1654385,0.000003393374,0.0005689492,0.0467406,0.7787349,0.00008104288],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.07911254,0.0004409455,0.00007496568,0.01695388,0.0007829492,0.0001407156,6.896973e-7,0.0001293528,0.902364],"genre_scores_gemma":[0.9662806,0.0001341018,0.001769599,0.002049926,0.0008159388,0.000005965461,0.000001709782,0.000003181068,0.02893898],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.887168,"threshold_uncertainty_score":0.9967143,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02815779110530803,"score_gpt":0.2678240799523792,"score_spread":0.2396662888470711,"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."}}