{"id":"W4401280208","doi":"10.29173/jchla29741","title":"Preprint Pointers From a Long COVID Scoping Review: Considerations for Source Selection and Searching","year":2024,"lang":"en","type":"article","venue":"Journal of the Canadian Health Libraries Association / Journal de l Association de bilbiothèques de la santé du Canada","topic":"Academic Publishing and Open Access","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Agency for Drugs and Technologies in Health","funders":"","keywords":"Preprint; Coronavirus disease 2019 (COVID-19); Terminology; Computer science; World Wide Web; Server; Selection (genetic algorithm); Data science; Medicine; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","scholarly_communication"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.4953808,0.00262546,0.008797464,0.05588109,0.009238262,0.02795584,0.008469107,0.01587018,0.06670731],"category_scores_gemma":[0.8179867,0.004965201,0.008973971,0.05105457,0.007206962,0.02309114,0.02606519,0.009584886,0.02174317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01808066,"about_ca_system_score_gemma":0.09902267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007052737,"about_ca_topic_score_gemma":0.01838942,"domain_scores_codex":[0.3914391,0.3145797,0.2286461,0.009370954,0.05272011,0.003244068],"domain_scores_gemma":[0.07942194,0.7196135,0.04710206,0.0378826,0.1107371,0.005242921],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009471395,0.0000820904,0.002300082,0.3071936,0.001438751,0.001696128,0.01461745,0.0006937351,0.001255515,0.04324143,0.3568667,0.2696674],"study_design_scores_gemma":[0.0004511461,0.0001437434,0.001420605,0.3494172,0.001018424,0.0007647763,0.004858699,0.0005702412,0.000569286,0.04012693,0.6003202,0.0003387227],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"methods","genre_scores_codex":[0.006000452,0.150856,0.1830547,0.4370275,0.06629623,0.0580723,0.02949667,0.006246472,0.0629497],"genre_scores_gemma":[0.03176373,0.08817095,0.5463471,0.1045627,0.0112158,0.1758017,0.01457454,0.006314307,0.02124915],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9720442,"threshold_uncertainty_score":0.6222852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0261969669861336,"score_gpt":0.3621180741677582,"score_spread":0.3359211071816245,"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."}}