{"id":"W2067425171","doi":"10.1080/01462679.2012.685420","title":"Three Libraries, Three Weeding Projects: Collaborative Weeding Projects Within a Shared Print Repository","year":2012,"lang":"en","type":"article","venue":"Collection Management","topic":"Library Collection Development and Digital Resources","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University; University of Guelph","funders":"","keywords":"Scope (computer science); Library science; World Wide Web; Political science; Computer science; Business","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":[0.04994237,0.0007821579,0.0006694946,0.005863634,0.01528268,0.01623865,0.004139268,0.002778875,0.009413487],"category_scores_gemma":[0.0536646,0.001424782,0.001178977,0.007663067,0.005776833,0.01116584,0.02294184,0.003761738,0.00325808],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007150774,"about_ca_system_score_gemma":0.03940284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007402345,"about_ca_topic_score_gemma":0.02302962,"domain_scores_codex":[0.9607397,0.01978493,0.002571165,0.003022122,0.01032607,0.003556033],"domain_scores_gemma":[0.9207261,0.01736247,0.005731198,0.0152127,0.01197234,0.02899524],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008301207,0.002815641,0.02155991,0.001405508,0.0001807388,0.002447555,0.07788116,0.002439702,0.008927971,0.02412483,0.05250657,0.8048804],"study_design_scores_gemma":[0.0004948771,0.003505711,0.03634111,0.001542711,0.0003225107,0.002273391,0.1796156,0.006392449,0.02474462,0.03421666,0.7098974,0.0006529694],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4524294,0.005460135,0.333909,0.02510974,0.001101843,0.02161503,0.001252167,0.01173996,0.1473826],"genre_scores_gemma":[0.4446601,0.001750478,0.4788475,0.00220499,0.0002558624,0.00470456,0.001463181,0.001939682,0.06417361],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9837614,"threshold_uncertainty_score":0.2641237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03376460625499757,"score_gpt":0.2206818076422837,"score_spread":0.1869172013872861,"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."}}