{"id":"W2116759887","doi":"10.1089/omi.2013.0034","title":"Crowd-Funded Micro-Grants for Genomics and “Big Data”: An Actionable Idea Connecting Small (Artisan) Science, Infrastructure Science, and Citizen Philanthropy","year":2013,"lang":"en","type":"article","venue":"OMICS A Journal of Integrative Biology","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Children's Hospital; University of Toronto; McGill University","funders":"National Institute on Minority Health and Health Disparities","keywords":"Big data; Citizen science; Data science; Commons; Crowdsourcing; Public relations; Political science; Engineering ethics; World Wide Web; Computer science; Engineering; Biology","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0320013,0.0009899192,0.0005310167,0.002585792,0.009572099,0.01649911,0.003185521,0.01159949,0.008429303],"category_scores_gemma":[0.03674951,0.0006942701,0.00112628,0.001757069,0.04014765,0.02209647,0.01684955,0.009314795,0.001346337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00656337,"about_ca_system_score_gemma":0.01419933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002854841,"about_ca_topic_score_gemma":0.003319686,"domain_scores_codex":[0.9704014,0.02042274,0.0005855687,0.002501915,0.003661549,0.00242682],"domain_scores_gemma":[0.9703423,0.01801033,0.002131102,0.002446209,0.001944871,0.005125264],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004707315,0.00008017806,0.0007014277,0.0001329687,0.00001819615,0.0001618443,0.006171342,0.0006436586,0.0003135854,0.9613975,0.01389291,0.01643924],"study_design_scores_gemma":[0.0001108171,0.0001156859,0.0005223952,0.0003597516,0.00002826927,0.0001143958,0.006477116,0.001337207,0.0004387755,0.7473535,0.2430527,0.00008943676],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.04054409,0.00482527,0.1413669,0.5347078,0.006987251,0.001029384,0.0002150775,0.0006985949,0.2696256],"genre_scores_gemma":[0.8064658,0.002616978,0.08487772,0.05792333,0.002354917,0.002149236,0.0000864192,0.0002215554,0.04330402],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9679987,"threshold_uncertainty_score":0.1692411,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.035480818521776,"score_gpt":0.318096018959975,"score_spread":0.282615200438199,"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."}}