{"id":"W3104688252","doi":"10.1101/2020.11.08.361014","title":"Mapping the planet’s critical natural assets","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Conservation, Biodiversity, and Resource Management","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Nature Conservancy of Canada","funders":"","keywords":"Biodiversity; Natural (archaeology); Environmental resource management; Ecosystem services; Natural resource economics; Scale (ratio); Business; Climate change; Geography; Ecosystem; Ecology; Environmental science; Economics; Biology; 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":[0.0002483174,0.0002461641,0.00008771203,0.003287111,0.0003451606,0.001010652,0.0002405759,0.000214389,0.004309603],"category_scores_gemma":[0.0009587954,0.00006113407,0.0001062095,0.003565637,0.0002837337,0.0008121031,0.0009741603,0.0003481705,0.0006262067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006353448,"about_ca_system_score_gemma":0.0006409666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01021609,"about_ca_topic_score_gemma":0.009925893,"domain_scores_codex":[0.9998664,0.00002378843,0.000005427167,0.00002605194,0.0000499142,0.00002840318],"domain_scores_gemma":[0.9994934,0.00009402792,0.0001480266,0.00003855448,0.0001500099,0.00007605222],"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":[0.0001211457,0.00008907876,0.350545,0.00102902,0.0001639805,0.0005577382,0.002396316,0.008683107,0.01411968,0.03917626,0.07976878,0.5033498],"study_design_scores_gemma":[0.00001678936,0.00006604531,0.6238994,0.0004885553,0.00007346802,0.0005803535,0.006971419,0.01046573,0.007623201,0.03528334,0.3144798,0.00005195671],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.77802,0.005879415,0.0167776,0.00367937,0.0001066556,0.00008601791,0.03023626,0.0004754632,0.1647392],"genre_scores_gemma":[0.978363,0.002185664,0.009917367,0.0001096892,0.00003076383,0.00004540897,0.007027639,0.000035226,0.002285179],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01021609,"threshold_uncertainty_score":0.02031326,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02323634170060739,"score_gpt":0.2034973565900565,"score_spread":0.1802610148894492,"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."}}