{"id":"W4390837502","doi":"10.1016/j.jglr.2023.102273","title":"Species distribution models effectively predict the detection of Dreissena spp. in two connecting waters of the Laurentian Great Lakes","year":2024,"lang":"en","type":"article","venue":"Journal of Great Lakes Research","topic":"Aquatic Invertebrate Ecology and Behavior","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"Fisheries and Oceans Canada; U.S. Fish and Wildlife Service; Michigan Department of Natural Resources; Central Michigan University","keywords":"Dreissena; Habitat; Abundance (ecology); Species distribution; Ecology; Tributary; Fishery; Invasive species; Geography; Environmental science; Biology; Cartography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0007926672,0.0006974277,0.0002689584,0.001152039,0.0003477608,0.0007413614,0.0003414126,0.0003481559,0.0005773905],"category_scores_gemma":[0.001937224,0.0004454135,0.0005954473,0.0004931063,0.0001906824,0.0004789556,0.0003415649,0.0003096555,0.0001717673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007406352,"about_ca_system_score_gemma":0.000743548,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04361984,"about_ca_topic_score_gemma":0.06321159,"domain_scores_codex":[0.9998572,0.00004485655,0.00001093294,0.00004899447,0.00001510077,0.0000228135],"domain_scores_gemma":[0.9992728,0.0004119838,0.000122922,0.00002717811,0.0001072621,0.00005787776],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001666883,0.00009814851,0.7505864,0.00002969497,0.0002056727,0.000103201,0.0001471363,0.2266031,0.001666061,0.0002023877,0.001045892,0.01914568],"study_design_scores_gemma":[0.00001621404,0.00005116282,0.1625278,0.00001015561,0.00003693892,0.00004589487,0.0001271624,0.8360156,0.0003530804,0.0003427397,0.0004585421,0.00001467585],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954781,0.00006443883,0.00342221,0.00007585643,0.00000389587,0.000008251915,0.0004374204,0.0001303049,0.0003795031],"genre_scores_gemma":[0.9950447,0.00003639328,0.003580128,0.00002038348,0.000003528974,0.00001409502,0.0009278449,0.00001258588,0.0003602641],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9563801,"threshold_uncertainty_score":0.08673191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03925442585495666,"score_gpt":0.3102088817498476,"score_spread":0.2709544558948909,"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."}}