{"id":"W3204799586","doi":"10.1101/2021.09.30.462145","title":"Why can we detect lianas from space?","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Liana; Canopy; Remote sensing; Scale (ratio); Spectral signature; Environmental science; Biology; Ecology; Geography; Cartography","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":[0.003088969,0.0007478715,0.0009300213,0.001220405,0.0006730042,0.003809463,0.001447396,0.001744844,0.002866638],"category_scores_gemma":[0.009891747,0.0004802608,0.0007091553,0.0009853157,0.001657181,0.006326224,0.001729123,0.001688447,0.002355135],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006316169,"about_ca_system_score_gemma":0.0005235086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00504406,"about_ca_topic_score_gemma":0.006227088,"domain_scores_codex":[0.9988568,0.0003976503,0.00004828506,0.000387492,0.0001715665,0.0001381592],"domain_scores_gemma":[0.9971878,0.001020523,0.0004213028,0.0005370369,0.0006001158,0.0002332525],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006269654,0.0002195041,0.4375279,0.001471903,0.0006448263,0.0008193892,0.001829968,0.02919011,0.05618965,0.01649936,0.02565563,0.4293248],"study_design_scores_gemma":[0.0001172516,0.0006560983,0.3009683,0.001619674,0.0004960161,0.002703547,0.007292406,0.3468034,0.03673218,0.2050842,0.09686022,0.0006667424],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6277614,0.01779884,0.2932813,0.02895422,0.00181957,0.000145981,0.003190451,0.005729083,0.02131914],"genre_scores_gemma":[0.940348,0.002211637,0.05117449,0.002638338,0.0004201653,0.00006424454,0.0009850223,0.0003095592,0.001848456],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00504406,"threshold_uncertainty_score":0.0163362,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008626345237883441,"score_gpt":0.1846426325022502,"score_spread":0.1760162872643668,"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."}}