{"id":"W2605169404","doi":"10.1038/srep45908","title":"Linear filtering reveals false negatives in species interaction data","year":2017,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Plant and animal studies","field":"Agricultural and Biological Sciences","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"Universiteit Gent; Vlaamse regering; Vlaams Supercomputer Centrum","keywords":"False positives and false negatives; Filter (signal processing); Computer science; Interaction; False positive paradox; Sampling (signal processing); Data mining; Pattern recognition (psychology); Artificial intelligence; Machine learning","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":[0.01902027,0.001195103,0.001574178,0.002421652,0.001428407,0.001940751,0.001306948,0.001801333,0.0008275735],"category_scores_gemma":[0.06505857,0.0005668071,0.001321604,0.001928891,0.001558887,0.001591297,0.0014765,0.001571426,0.0004760501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008531096,"about_ca_system_score_gemma":0.0007636055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003434923,"about_ca_topic_score_gemma":0.00444362,"domain_scores_codex":[0.9830872,0.006431917,0.001409452,0.00447571,0.003643296,0.0009524431],"domain_scores_gemma":[0.9036506,0.08088747,0.005011399,0.006689588,0.00320969,0.0005512718],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003061404,0.0006209929,0.5293725,0.00171723,0.002170893,0.004612467,0.00295726,0.09744464,0.05935562,0.01112308,0.01696254,0.2706014],"study_design_scores_gemma":[0.00009253983,0.0004155475,0.2208209,0.0002038733,0.0006141781,0.002936041,0.0008490867,0.6884162,0.04742999,0.02816591,0.009883711,0.0001719917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5886182,0.001584884,0.4034733,0.0006613888,0.000230434,0.0001645555,0.001787594,0.002012849,0.001466813],"genre_scores_gemma":[0.919269,0.0001546924,0.07524838,0.0004631614,0.00009504977,0.0001147768,0.00372423,0.0001562799,0.0007744139],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01902027,"threshold_uncertainty_score":0.1005901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.206524993653177,"score_gpt":0.3074238937598517,"score_spread":0.1008989001066747,"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."}}