{"id":"W4247564337","doi":"10.5755/j01.erem.56.2.272","title":"Profundal-Pelagic Macrocrustacean Abundance in Boreal Lakes Before and After Experimental Clearcut Logging","year":2011,"lang":"en","type":"article","venue":"Environmental Research Engineering and Management","topic":"Aquatic Invertebrate Ecology and Behavior","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Clearcutting; Taiga; Boreal; Pelagic zone; Ecology; Logging; Abundance (ecology); Environmental science; Oceanography; Biology; Geology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.000177276,0.0001641011,0.0001374757,0.0003631252,0.0004512128,0.000368765,0.0001476729,0.0001909588,0.000479411],"category_scores_gemma":[0.0004823504,0.0001496187,0.0001639706,0.0002380557,0.0003632104,0.0002932028,0.0002394532,0.0001804229,0.00007292551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006966448,"about_ca_system_score_gemma":0.0003856211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08093069,"about_ca_topic_score_gemma":0.2718978,"domain_scores_codex":[0.999889,0.00001510405,0.000008832571,0.00002688268,0.00002121068,0.00003894947],"domain_scores_gemma":[0.9995697,0.00003535189,0.0001467187,0.00001834732,0.00008101139,0.0001489151],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008756856,0.0001726137,0.9851136,0.00001240196,0.00004022871,0.0001185356,0.0006122013,0.00006476953,0.01033793,0.00001198671,0.00008578605,0.002554108],"study_design_scores_gemma":[0.000001992914,0.00009387456,0.9995757,6.181536e-7,0.000005040676,0.00001434449,0.00009723056,0.00002226083,0.0001586833,0.000001360193,0.00002780654,0.000001128226],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9998736,0.00001536172,0.00000392989,0.000001873113,5.923246e-7,0.000001211199,0.00004350857,6.025538e-7,0.0000592628],"genre_scores_gemma":[0.999644,0.00001910696,0.00002232124,0.000006816637,0.000001003415,0.000003595464,0.0001574518,3.680919e-7,0.0001452685],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08093069,"threshold_uncertainty_score":0.1609193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01813537072908811,"score_gpt":0.2497406879778641,"score_spread":0.231605317248776,"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."}}