{"id":"W2963681745","doi":"10.1101/328245","title":"Revealing biases in the sampling of ecological interaction networks","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Plant and animal studies","field":"Agricultural and Biological Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Université de Montréal; Université de Sherbrooke","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de São Paulo; National Institute for Mathematical and Biological Synthesis; University of Tennessee, Knoxville; National Science Foundation","keywords":"Sampling (signal processing); Ecological network; Computer science; Bipartite graph; Modular design; Scale (ratio); Sampling design; Complex network; Community structure; Network topology; Data mining; Theoretical computer science; Ecology; Statistics; Mathematics; Biology; Graph; Geography; Cartography; Population; Computer network","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01476563,0.0004750986,0.0005739108,0.00168699,0.0004976291,0.0009475901,0.001135216,0.0006705415,0.0004834929],"category_scores_gemma":[0.08642007,0.0004841157,0.000501461,0.001269062,0.001388539,0.001615087,0.001504181,0.00104234,0.000136259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001210647,"about_ca_system_score_gemma":0.0006504355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002566067,"about_ca_topic_score_gemma":0.00327555,"domain_scores_codex":[0.9903785,0.006210136,0.0004162475,0.001423137,0.001305858,0.000266053],"domain_scores_gemma":[0.9110241,0.06914482,0.006797424,0.008600985,0.00382809,0.0006044915],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007588177,0.0002740235,0.4263151,0.0007785977,0.0008564862,0.0004248951,0.001935859,0.3523846,0.06767541,0.0377726,0.001723019,0.1091006],"study_design_scores_gemma":[0.00005616385,0.0002272765,0.08945577,0.00008299023,0.0001253328,0.0003582862,0.0002981357,0.822427,0.03545321,0.04859534,0.002859771,0.00006076463],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5751009,0.0002909674,0.4225744,0.00016814,0.00002321243,0.0001276938,0.0003952883,0.0003083977,0.001010949],"genre_scores_gemma":[0.9109498,0.000103337,0.08791023,0.00008441506,0.00001669315,0.0001484659,0.0005350778,0.00005069052,0.0002012297],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9852344,"threshold_uncertainty_score":0.07808906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08459689231986321,"score_gpt":0.2502516390560151,"score_spread":0.1656547467361518,"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."}}