{"id":"W2514729657","doi":"10.1111/cobi.12809","title":"Feasibility of using high‐resolution satellite imagery to assess vertebrate wildlife populations","year":2016,"lang":"en","type":"article","venue":"Conservation Biology","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"Government of Nunavut","funders":"Government of Nunavut; DigitalGlobe Foundation","keywords":"Wildlife; Geography; Satellite imagery; Remote sensing; Habitat; Environmental resource management; Wildlife conservation; Satellite; Computer science; Ecology; Environmental science; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.03363241,0.0004355332,0.0003211052,0.006790138,0.0004653251,0.002792407,0.0008684456,0.0008583152,0.0009838411],"category_scores_gemma":[0.04776124,0.0004378498,0.0009269966,0.003893219,0.001226217,0.004933821,0.001091508,0.0005962717,0.0002823308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004341903,"about_ca_system_score_gemma":0.0008631171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001853486,"about_ca_topic_score_gemma":0.005618185,"domain_scores_codex":[0.9899591,0.006305682,0.00102825,0.0006957924,0.001889726,0.0001214877],"domain_scores_gemma":[0.9324053,0.04770526,0.006159164,0.002645062,0.01065167,0.000433651],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003954715,0.0001145065,0.590524,0.003425018,0.001190283,0.0002541044,0.001356138,0.002267631,0.004469209,0.001752534,0.0008608085,0.3933903],"study_design_scores_gemma":[0.0001135582,0.002310858,0.9112101,0.005263275,0.002842007,0.002277941,0.007519454,0.01433931,0.01159217,0.01039322,0.03185594,0.0002821727],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.770145,0.07540114,0.09367366,0.004620814,0.0003277278,0.001370845,0.001949905,0.0001756194,0.05233535],"genre_scores_gemma":[0.8970032,0.01916955,0.08085144,0.0004435033,0.0002617254,0.0003407074,0.0006784693,0.00002790976,0.001223387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03363241,"threshold_uncertainty_score":0.1778674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.111630570762315,"score_gpt":0.317780172746491,"score_spread":0.206149601984176,"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."}}