{"id":"W6950135270","doi":"10.5281/zenodo.6917218","title":"Small Data projects/Big Data research: contemporary problems and historical solutions","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Presentation (obstetrics); Minor (academic); Data collection; Small data","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gpt","categories":[],"domain":null,"study_design":"theoretical_or_conceptual","genre":"other","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"opus","categories":["metaresearch"],"domain":"reproducibility","study_design":"not_applicable","genre":"other","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.01312295,0.0008153582,0.0006937172,0.003204112,0.002562999,0.01166971,0.001828076,0.003143846,0.03660687],"category_scores_gemma":[0.02613763,0.0007441668,0.0005144121,0.00903878,0.008108819,0.01488686,0.004510582,0.007300331,0.02013444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002724856,"about_ca_system_score_gemma":0.003788565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002401268,"about_ca_topic_score_gemma":0.002739184,"domain_scores_codex":[0.9945911,0.001967372,0.0004067258,0.0005676986,0.002220814,0.0002463844],"domain_scores_gemma":[0.982031,0.009980603,0.0006880359,0.002044076,0.00401337,0.00124295],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003878583,0.00001478677,0.0001710401,0.0008329712,0.000006794819,0.00008759749,0.0009876508,0.00009040028,0.0005042852,0.1405931,0.7565339,0.1001386],"study_design_scores_gemma":[0.000004365787,0.00001156457,0.000296503,0.0005746163,0.000002939157,0.0001631105,0.000910203,0.0001034847,0.0002272308,0.0477473,0.9499421,0.00001666121],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.002243747,0.1385695,0.0500669,0.5254041,0.1155028,0.0002540805,0.003216899,0.001729421,0.1630125],"genre_scores_gemma":[0.04745889,0.306842,0.07819836,0.07127804,0.1005021,0.0009457374,0.005719028,0.005594656,0.3834612],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.997437,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9315210701659241,"score_gpt":0.3777249985773631,"score_spread":0.553796071588561,"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."}}