{"id":"W6929660157","doi":"10.5061/dryad.1vr6g3f","title":"Data from: Temporal variation in spatial genetic structure during population outbreaks: distinguishing among different potential drivers of spatial synchrony","year":2019,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"TGF-β signaling in diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Canadian Forest Service; Université de Montréal","funders":"","keywords":"Biological dispersal; Population; Spatial analysis; Spatial ecology; Genetic variation; Genetic structure; Genetic diversity; Spatial variability","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001153267,0.0002955866,0.0003806391,0.001560909,0.0005152625,0.000758158,0.0005665114,0.0003720334,0.007120006],"category_scores_gemma":[0.004224872,0.0001547426,0.0003270435,0.003143044,0.0002822977,0.0003253335,0.0005776511,0.0004250953,0.002288289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001690089,"about_ca_system_score_gemma":0.001770659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3484908,"about_ca_topic_score_gemma":0.5323991,"domain_scores_codex":[0.9993212,0.00008782233,0.00006810463,0.0001686403,0.0002474471,0.0001068033],"domain_scores_gemma":[0.9958171,0.0008070115,0.0007701211,0.0005953946,0.001747771,0.0002626557],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008279037,0.0001188009,0.8369842,0.0006429027,0.0002808637,0.0003271573,0.001384465,0.002447383,0.01058232,0.0007427359,0.06982775,0.07583352],"study_design_scores_gemma":[0.00002773402,0.00004067565,0.9768039,0.00004393094,0.00002587426,0.00005060859,0.0002601647,0.001135712,0.001006091,0.0001112471,0.02047379,0.00002025909],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.445769,0.0003074557,0.004931126,0.0006185221,0.0000797468,0.0002806551,0.5365449,0.001032221,0.01043631],"genre_scores_gemma":[0.6148345,0.0002618748,0.01104493,0.0002522201,0.00004657046,0.0006442515,0.36426,0.0001732882,0.008482373],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3484908,"threshold_uncertainty_score":0.6929248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01755384407396485,"score_gpt":0.2381225578262024,"score_spread":0.2205687137522375,"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."}}