{"id":"W2939201034","doi":"10.1002/ecs2.2701","title":"Long‐term population dynamics of dreissenid mussels (<i>Dreissena polymorpha</i> and <i>D. rostriformis</i>): a cross‐system analysis","year":2019,"lang":"en","type":"article","venue":"Ecosphere","topic":"Aquatic Invertebrate Ecology and Behavior","field":"Environmental Science","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Museum of Nature","funders":"U.S. Army Corps of Engineers; Deutsche Forschungsgemeinschaft; U.S. Geological Survey; Belarusian Republican Foundation for Fundamental Research; Minnesota Department of Natural Resources; New York State Department of Environmental Conservation; U.S. Army; U.S. Environmental Protection Agency; National Science Foundation","keywords":"Dreissena; Zebra mussel; Population; Ecology; Biology; Invasive species; Range (aeronautics); Population decline; Bivalvia; Fishery; Mussel; Mollusca; Habitat; Demography","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":[0.001647145,0.0003177165,0.0004879223,0.00109117,0.0003896599,0.0006271737,0.0004626673,0.0004006013,0.0008840397],"category_scores_gemma":[0.001505856,0.0002433175,0.001136546,0.0007268556,0.0002710411,0.0005401604,0.0008033182,0.0003903336,0.0001649087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006492303,"about_ca_system_score_gemma":0.0002857507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01293662,"about_ca_topic_score_gemma":0.01526242,"domain_scores_codex":[0.999447,0.0001552626,0.00005370386,0.0001850492,0.00008051233,0.00007829949],"domain_scores_gemma":[0.9983445,0.0005244037,0.0003776373,0.0002209129,0.0003340916,0.0001984722],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009958751,0.00007053708,0.9897787,0.00002465359,0.0008542683,0.00007914197,0.0001905424,0.001446154,0.003764034,0.00004854471,0.0001822413,0.003461544],"study_design_scores_gemma":[0.000002947233,0.0001039501,0.996033,0.000004088535,0.0001441041,0.00004955918,0.0001311724,0.002939666,0.0003758112,0.00001692163,0.0001907384,0.00000804706],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9992501,0.00005129056,0.0002499513,0.00001090725,0.000002158907,0.000005053389,0.0003192858,0.000006287065,0.0001049442],"genre_scores_gemma":[0.9983462,0.00003287226,0.0003475274,0.00001216597,0.00000326787,0.00001466498,0.001074723,0.000003507732,0.0001651327],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01293662,"threshold_uncertainty_score":0.02572262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003343479085658343,"score_gpt":0.2112050329652373,"score_spread":0.2078615538795789,"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."}}