{"id":"W7091716473","doi":"10.5281/zenodo.17363494","title":"Local variation in keystone predator abundance maximizes landscape-scale biodiversity","year":2025,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Oil Spill Detection and Mitigation","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"","keywords":"Biodiversity; Abundance (ecology); Variation (astronomy); Predator; Yield (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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001354586,0.001905123,0.001138045,0.002273847,0.0005661772,0.002295332,0.002300023,0.001450397,0.1032489],"category_scores_gemma":[0.004854162,0.0007974485,0.00166288,0.002937537,0.0004215522,0.001131595,0.00136301,0.001507932,0.08487889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001247346,"about_ca_system_score_gemma":0.001567225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01522738,"about_ca_topic_score_gemma":0.04959151,"domain_scores_codex":[0.9993266,0.0001220137,0.00007769147,0.0002735084,0.00009197993,0.000108281],"domain_scores_gemma":[0.9983234,0.0005777783,0.0001667978,0.000541012,0.000242557,0.0001483768],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.000108717,0.00003554292,0.003735264,0.0009270099,0.000100893,0.00002962633,0.00006093003,0.001235631,0.0003625436,0.001102371,0.9886518,0.003649631],"study_design_scores_gemma":[0.0008074301,0.00005106252,0.01978586,0.0004443766,0.0001202303,0.0001433439,0.0001656339,0.00449601,0.001438118,0.007515095,0.9649483,0.00008458575],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002951929,0.00002040151,0.0002017752,0.00003557958,0.0000186521,0.00001132806,0.9980255,0.0008380185,0.0005536245],"genre_scores_gemma":[0.001425114,0.00003716932,0.001146161,0.0000417237,0.000007378744,0.0001444804,0.9959427,0.0003741606,0.000881078],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1032489,"threshold_uncertainty_score":0.345402,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01041080102489936,"score_gpt":0.2046223358337919,"score_spread":0.1942115348088925,"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."}}