{"id":"W2782712271","doi":"10.1111/nrm.12156","title":"A guide to calculating habitat‐quality metrics to inform conservation of highly mobile species","year":2018,"lang":"en","type":"article","venue":"Natural Resource Modeling","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"National Institute for Mathematical and Biological Synthesis","keywords":"Occupancy; Metric (unit); Habitat; Computer science; Centrality; Quality (philosophy); Resource (disambiguation); Graph; Data science; Data quality; Data mining; Environmental resource management; Ecology; Geography; Environmental science; Statistics; Theoretical computer science; Mathematics; Engineering","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":[],"category_scores_codex":[0.0005428809,0.0001310779,0.000182384,0.00009145869,0.0002033206,0.00004108741,0.000240815,0.0000706085,0.00172728],"category_scores_gemma":[0.000882488,0.000118695,0.00007006399,0.001084894,0.00007952789,0.0001324227,0.0003149506,0.0001132414,0.0003717632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006231541,"about_ca_system_score_gemma":0.000008822148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009760569,"about_ca_topic_score_gemma":0.000842259,"domain_scores_codex":[0.9983432,0.00002819597,0.000455721,0.0002574672,0.0006125235,0.0003028908],"domain_scores_gemma":[0.9992978,0.00008565346,0.00009645681,0.0002491203,0.0001221128,0.0001488395],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000756825,0.0002811567,0.07105107,0.0001400293,0.00007440348,0.000005806036,0.01822163,0.1512585,0.5577416,0.007455805,0.1196292,0.07338399],"study_design_scores_gemma":[0.0006731286,0.0003741701,0.04489066,0.00007255845,0.00002365562,0.000003682129,0.01085557,0.2632416,0.02902328,0.00006964165,0.6500898,0.0006822378],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9708315,0.00003187596,0.005615328,0.0007108754,0.00008377117,0.0002885385,0.00004028855,0.00005275556,0.02234511],"genre_scores_gemma":[0.9930259,0.000001898901,0.003616481,0.001853868,0.00005803064,0.00001682918,0.00003284978,0.00001017018,0.001383977],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5304606,"threshold_uncertainty_score":0.9991853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05712934691262329,"score_gpt":0.3309234665795291,"score_spread":0.2737941196669059,"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."}}