{"id":"W2557608074","doi":"10.3897/bdj.4.e10671","title":"Testing the Global Malaise Trap Program – How well does the current barcode reference library identify flying insects in Germany?","year":2016,"lang":"en","type":"article","venue":"Biodiversity Data Journal","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":134,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Bundesministerium für Bildung und Forschung; Genome Canada","keywords":"Barcode; Biodiversity; DNA barcoding; Ecology; Species diversity; Global biodiversity; Sampling (signal processing); Habitat; Geography; Biology; 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":"codex-gemma-dda1882f352a","candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004675974,0.0002354812,0.0001596154,0.00003528107,0.001496673,0.0003492206,0.002853001,0.0000592194,0.0007315025],"category_scores_gemma":[0.0001254259,0.0001089234,0.00006343424,0.0003401238,0.0009227954,0.002271141,0.005473167,0.0004534448,0.0008782628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003300831,"about_ca_system_score_gemma":0.00001533172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001294266,"about_ca_topic_score_gemma":0.00009943522,"domain_scores_codex":[0.9978887,0.0002434936,0.0002079938,0.000503995,0.0006397559,0.0005160075],"domain_scores_gemma":[0.9987327,0.0001863674,0.0002159814,0.0007041417,0.000006943667,0.0001538148],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002280553,0.0001019186,0.9001457,0.000005775594,0.00001898432,0.00005117258,0.0001141433,0.000001915956,0.000423054,0.000002332248,0.02659496,0.07251725],"study_design_scores_gemma":[0.000434137,0.00004307463,0.9305477,0.00006083164,0.00004128344,0.00004504482,0.0004289525,0.00001977199,0.00007138928,0.000124341,0.06798776,0.0001957265],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9888806,0.0003090454,0.00003155189,0.007795878,0.0004282583,0.0004282311,0.001155884,0.0000637334,0.0009067751],"genre_scores_gemma":[0.9961317,0.001320515,0.002040437,0.000290905,0.00009645263,0.000003959462,0.00003204942,0.000005826469,0.00007815065],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07232153,"threshold_uncertainty_score":0.9998997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07861456649873019,"score_gpt":0.2696637984746262,"score_spread":0.191049231975896,"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."}}