{"id":"W6929904344","doi":"10.5061/dryad.4d12c92","title":"Data from: Consumer-resource interactions along urbanization gradients drive natural selection","year":2018,"lang":"en","type":"dataset","venue":"Data Archiving and Networked Services (DANS)","topic":"Signaling Pathways in Disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Urbanization; Natural selection; Habitat; Gall; Predation; Adaptation (eye)","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":[],"consensus_categories":[],"category_scores_codex":[0.001091358,0.001494357,0.001045534,0.002296701,0.0006726329,0.001793022,0.001981374,0.001404354,0.04377129],"category_scores_gemma":[0.006032753,0.0004839616,0.0009318095,0.003863294,0.0004411427,0.0008873534,0.001757255,0.001475092,0.03193942],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001193088,"about_ca_system_score_gemma":0.001780068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01934116,"about_ca_topic_score_gemma":0.03488187,"domain_scores_codex":[0.9992299,0.0001191835,0.0001086422,0.0002258524,0.0001920735,0.0001244193],"domain_scores_gemma":[0.99805,0.0006667929,0.0003139744,0.0003752538,0.0003706359,0.0002233847],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002737718,0.00006762272,0.01261872,0.002208733,0.0001875313,0.0001142819,0.0001481731,0.001181001,0.0007399106,0.001290448,0.9749941,0.006175826],"study_design_scores_gemma":[0.0005677237,0.0000309147,0.03626239,0.0004009733,0.00008375706,0.0001308824,0.0001627612,0.001022145,0.0009198287,0.001739406,0.9586253,0.00005394411],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0007262737,0.00006139954,0.00007776884,0.00009290596,0.00001460234,0.00001002072,0.9983149,0.0002339198,0.0004684146],"genre_scores_gemma":[0.001929262,0.0000718343,0.0003833427,0.00004066212,0.000005181464,0.00008705266,0.9968643,0.00006938649,0.0005489463],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04377129,"threshold_uncertainty_score":0.1464296,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01980716814096927,"score_gpt":0.278874058596368,"score_spread":0.2590668904553987,"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."}}