{"id":"W2885625138","doi":"10.1111/ecog.03724","title":"Fine scale waterbody data improve prediction of waterbird occurrence despite coarse species data","year":2018,"lang":"en","type":"article","venue":"Ecography","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Česká Zemědělská Univerzita v Praze","keywords":"Habitat; Land cover; Species distribution; Grid cell; Range (aeronautics); Scale (ratio); Ecology; Spatial ecology; Geography; Remote sensing; Spatial distribution; Spatial analysis; Environmental science; Physical geography; Cartography; Grid; Land use; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003179042,0.0001444072,0.0001503028,0.00005371226,0.00012997,0.00004340354,0.001291473,0.00005764793,0.02859099],"category_scores_gemma":[0.00002800529,0.0001205957,0.0000415737,0.0003982522,0.0007697748,0.0006516108,0.001620104,0.0000805329,0.0009731142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003887759,"about_ca_system_score_gemma":0.000005258955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002190118,"about_ca_topic_score_gemma":0.001092679,"domain_scores_codex":[0.9985194,0.00002988958,0.000276082,0.0005763106,0.0002968602,0.0003014794],"domain_scores_gemma":[0.9980018,0.00001705601,0.0001109884,0.001747323,0.00002340175,0.00009945416],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005848579,0.0003983136,0.5842659,0.0000390281,0.00004231761,0.000002523151,0.0004336502,8.668237e-7,0.1160993,0.00003737816,0.2898885,0.008733648],"study_design_scores_gemma":[0.0006593554,0.0003442323,0.580287,0.00002443235,0.00006540905,0.000005748106,0.0005048434,0.001525756,0.04589282,0.00008929942,0.3702959,0.0003051527],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9594337,0.00004432249,0.0003511983,0.0002304623,0.0008084689,0.000242371,0.02704729,0.00009572627,0.01174644],"genre_scores_gemma":[0.9906002,0.00009782146,0.0003461737,0.00007835593,0.0001425871,0.000007638443,0.008515502,0.00000991354,0.0002018217],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0804074,"threshold_uncertainty_score":0.9998047,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06902803224855628,"score_gpt":0.2675844229272499,"score_spread":0.1985563906786936,"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."}}