{"id":"W3016216609","doi":"10.22541/au.157971234.43486031","title":"Co-occurrence is not evidence of ecological interaction","year":2020,"lang":"en","type":"dataset","venue":"Authorea","topic":"Plant and animal studies","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Université de Sherbrooke","funders":"","keywords":"Proxy (statistics); Co-occurrence; Ecology; Computer science; Null model; Spatial analysis; Data science; Geography; Psychology; Biology; Artificial intelligence; Machine learning; Remote sensing","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":[0.002570319,0.0007391679,0.001393257,0.002580273,0.0008655892,0.00238433,0.002553378,0.001548951,0.0269602],"category_scores_gemma":[0.01508567,0.0004566438,0.0008827291,0.00519009,0.0006963746,0.001729056,0.001962292,0.001581498,0.01974056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001211929,"about_ca_system_score_gemma":0.001309778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01317685,"about_ca_topic_score_gemma":0.02909249,"domain_scores_codex":[0.9976356,0.0005362261,0.0004251981,0.0007720477,0.0003073921,0.0003234757],"domain_scores_gemma":[0.9944785,0.002377647,0.0008976574,0.001237661,0.0006763517,0.0003322489],"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":[0.001004927,0.0001065213,0.06693082,0.005858072,0.0006398347,0.0002896836,0.0002361562,0.001382987,0.0006828754,0.004849316,0.9043849,0.01363384],"study_design_scores_gemma":[0.0006633003,0.00005944438,0.06887562,0.00129717,0.0002871736,0.000515831,0.0004210224,0.001237317,0.0006164469,0.006544111,0.9194162,0.0000665],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.004866929,0.0005364275,0.0002700174,0.0004100361,0.00009182901,0.00002643508,0.9916793,0.0000973002,0.002021754],"genre_scores_gemma":[0.01518377,0.0002259589,0.0007495982,0.0002969114,0.00003276599,0.0001528045,0.98217,0.00005259493,0.001135654],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0269602,"threshold_uncertainty_score":0.09019083,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1986406430542082,"score_gpt":0.327285678156794,"score_spread":0.1286450351025858,"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."}}