{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009543294,0.0002523981,0.0002394481,0.00008842252,0.0001882533,0.0004205432,0.0003691249,0.00008565898,0.0005770237],"category_scores_gemma":[0.000003657437,0.0002284859,0.0001486836,0.0001674711,0.00002443655,0.00007569048,0.002212066,0.000285411,0.0008165512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002703473,"about_ca_system_score_gemma":0.00009215001,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01675311,"about_ca_topic_score_gemma":0.00005726454,"domain_scores_codex":[0.9985914,0.00003293406,0.0001940257,0.0006262221,0.0002810913,0.0002743456],"domain_scores_gemma":[0.9993165,0.00003999878,0.00006589124,0.000365641,0.00005331999,0.0001586835],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003585857,0.00007113304,0.8774618,0.00005339653,0.0002926664,0.0000120685,0.003432437,0.01468223,0.01209521,0.002119682,0.005233081,0.08451042],"study_design_scores_gemma":[0.0006782111,0.0001053669,0.6380199,0.0007112714,0.0005064745,3.195626e-7,0.004734999,0.01420065,0.1242142,0.1921851,0.02203653,0.002607106],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.990246,0.0002846891,0.003305057,0.001067138,0.002303417,0.0003098324,0.0001314499,0.00009033913,0.002262094],"genre_scores_gemma":[0.9823422,0.00001658427,0.01179041,0.00009803557,0.002391513,0.00001792441,0.0001107663,0.00001809774,0.003214509],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.239442,"threshold_uncertainty_score":0.9999614,"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."}}