{"id":"W4409777433","doi":"10.52843/cassyni.qprxz7","title":"Leveling Up Citizen Science for (meta)genomic research","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Génome Québec; Genome Canada","keywords":"Citizen science; Data science; Computer science; Computational biology; Political science; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02211458,0.0008399168,0.0007386883,0.002351759,0.006357194,0.0143089,0.002399173,0.006220765,0.02203131],"category_scores_gemma":[0.0348753,0.0007810307,0.00128144,0.001883037,0.01135223,0.02108865,0.02998488,0.008117165,0.006661904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002698779,"about_ca_system_score_gemma":0.007112618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002274928,"about_ca_topic_score_gemma":0.004054429,"domain_scores_codex":[0.9809892,0.01246143,0.000316326,0.001730712,0.002921783,0.001580596],"domain_scores_gemma":[0.9740443,0.01269474,0.0008215806,0.005379708,0.00233418,0.004725503],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003996844,0.0008625094,0.008827423,0.00105916,0.0001322864,0.0008143351,0.04086717,0.003524972,0.01286867,0.5705618,0.09252734,0.2675546],"study_design_scores_gemma":[0.00006633199,0.000133376,0.001681797,0.0004303022,0.00002871917,0.000242082,0.01476657,0.005698249,0.003037555,0.4290893,0.5447613,0.00006447089],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08014379,0.00387929,0.5174084,0.1905753,0.003069283,0.001149775,0.0008563977,0.003262961,0.1996548],"genre_scores_gemma":[0.5084286,0.002639814,0.426066,0.01877309,0.001149304,0.001132689,0.001173427,0.001414548,0.03922257],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02211458,"threshold_uncertainty_score":0.1169545,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3500355949405324,"score_gpt":0.4303422421357606,"score_spread":0.08030664719522823,"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."}}