{"id":"W7062340308","doi":"","title":"Testing environmental DNA sampling and predictive modeling as means to investigate wood frog (Rana sylvatica) distribution in Alaska and Northern Canada","year":2017,"lang":"en","type":"other","venue":"ScholarWorks-UA (University of Alaska Fairbanks)","topic":"Advanced Power Generation Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sampling (signal processing); Distribution (mathematics); Environmental DNA; Species distribution; Spatial distribution","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001270705,0.0003564041,0.0004279722,0.0002387348,0.0002438099,0.00004794075,0.0003529931,0.0003693425,0.00002221552],"category_scores_gemma":[0.0001966688,0.0004863246,0.00003238856,0.0001726858,0.000182458,0.0003615711,0.0002816673,0.0006249069,0.000004561349],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004747854,"about_ca_system_score_gemma":0.0001203269,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01689891,"about_ca_topic_score_gemma":0.2356083,"domain_scores_codex":[0.9986638,0.00002791489,0.0001791148,0.0004880695,0.0002741897,0.0003669484],"domain_scores_gemma":[0.9991617,0.00005720414,0.0001538597,0.0004148649,0.00002903923,0.0001833909],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001521628,0.0001119054,0.1343998,0.000985772,0.001200715,0.0002853204,0.005242737,0.758016,0.004994601,0.0001851731,0.01295767,0.08146811],"study_design_scores_gemma":[0.008219936,0.000711902,0.0937031,0.01198687,0.001056274,0.00009780845,0.03592519,0.7686461,0.002471782,0.003315513,0.06585036,0.008015211],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9214602,0.001911753,0.0700326,0.0001781429,0.0002197327,0.0006954364,0.001094307,0.0004559646,0.00395194],"genre_scores_gemma":[0.9861283,0.0003579835,0.01088807,0.00001541027,0.00003944178,0.000002590282,0.0002226663,0.0001483158,0.002197239],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2187093,"threshold_uncertainty_score":0.9997588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01058124001588632,"score_gpt":0.17861238956636,"score_spread":0.1680311495504737,"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."}}