{"id":"W1944104092","doi":"10.1002/ece3.1640","title":"Effects of spatial scale of sampling on food web structure","year":2015,"lang":"en","type":"article","venue":"Ecology and Evolution","topic":"Plant and animal studies","field":"Agricultural and Biological Sciences","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"University of British Columbia; Division of Biological Infrastructure; Santa Fe Institute; National Science Foundation","keywords":"Species richness; Food web; Ecology; Spatial ecology; Archipelago; Sampling (signal processing); Scale (ratio); Ecosystem; Spatial analysis; Macroecology; Environmental science; Geography; Biology; Remote sensing; Cartography; Computer science","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":[0.01441632,0.0003537615,0.0004919598,0.0007306638,0.0005909064,0.0009546748,0.0008209848,0.0005371233,0.0007240732],"category_scores_gemma":[0.05876618,0.0004215779,0.0008085851,0.0009910166,0.00142017,0.001200508,0.001547395,0.0004670241,0.0001456281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004295285,"about_ca_system_score_gemma":0.0002990017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004025436,"about_ca_topic_score_gemma":0.00580555,"domain_scores_codex":[0.9864091,0.008089853,0.001139568,0.002047399,0.0019079,0.0004063165],"domain_scores_gemma":[0.9078835,0.06969977,0.00816815,0.01134697,0.002240505,0.0006611085],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004591388,0.00007422403,0.9489363,0.0002246947,0.0008830278,0.0002297333,0.0008043564,0.005932571,0.01887312,0.0007958904,0.0002224773,0.02256441],"study_design_scores_gemma":[0.00002021774,0.0002830669,0.986209,0.00002862992,0.0002020962,0.000270984,0.0003409999,0.007295671,0.003600063,0.0009811053,0.0007412257,0.0000268803],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.974719,0.000780253,0.02107372,0.0002190743,0.00004554234,0.0000788008,0.0002831681,0.00009639833,0.002704106],"genre_scores_gemma":[0.9953675,0.0001126499,0.004087924,0.0000680951,0.00001641633,0.00004776453,0.0001658666,0.0000224258,0.0001114644],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01441632,"threshold_uncertainty_score":0.07624173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02380318935690161,"score_gpt":0.2005189296203738,"score_spread":0.1767157402634721,"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."}}