{"id":"W4399773267","doi":"10.32942/x2rs58","title":"Genes from space: Leveraging Earth Observation satellites to monitor genetic diversity","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Space Science and Extraterrestrial Life","field":"Physics and Astronomy","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Diversity (politics); Space (punctuation); Earth (classical element); Earth observation; Genetic diversity; Computer science; Satellite; Remote sensing; Astrobiology; Computational biology; Geography; Biology; Aerospace engineering; Physics; Astronomy; Engineering; Political science; Sociology","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":[0.001187735,0.0004317669,0.0002950595,0.001430856,0.000424433,0.002118781,0.000418592,0.0004654397,0.001715169],"category_scores_gemma":[0.003222074,0.0001925706,0.0003761122,0.002393487,0.0007600714,0.001378617,0.001118024,0.0005518639,0.0008050381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005040899,"about_ca_system_score_gemma":0.00109913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01249964,"about_ca_topic_score_gemma":0.01561225,"domain_scores_codex":[0.9995052,0.0001073947,0.00002502838,0.000127653,0.0001996351,0.00003520578],"domain_scores_gemma":[0.9992509,0.0002140933,0.0001055331,0.0002055096,0.0001363534,0.00008754498],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003167091,0.00008384669,0.138457,0.0004933511,0.0002665819,0.0007864483,0.003915301,0.03215922,0.05091827,0.04556954,0.05362371,0.6734099],"study_design_scores_gemma":[0.0001404415,0.0001817697,0.1551098,0.0003888827,0.0002391703,0.0007456027,0.003576269,0.138603,0.05587359,0.195704,0.449167,0.000270378],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.307781,0.003929921,0.5719815,0.007405852,0.001192703,0.0004329242,0.02679079,0.02349007,0.05699529],"genre_scores_gemma":[0.4778076,0.002111477,0.49655,0.0007406228,0.0002851407,0.0002207849,0.01233748,0.001598609,0.00834828],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01249964,"threshold_uncertainty_score":0.02485377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04604040601307113,"score_gpt":0.2658234860476917,"score_spread":0.2197830800346206,"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."}}