{"id":"W7055632998","doi":"","title":"Combining Environmental Factors and Species Co-occurrence Patterns to Predict Species Abundance and Community Biomass: Method Development and Validation in Ontario Lakes","year":2024,"lang":"en","type":"other","venue":"Spectrum Research Repository (Concordia University)","topic":"Particle accelerators and beam dynamics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Abundance (ecology); Abiotic component; Ecosystem; Biodiversity; Community; Biomass (ecology); Community structure; Population; Relative abundance distribution; Ecosystem management","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006017478,0.0009387857,0.0006247016,0.0008264304,0.001419139,0.001110866,0.001325233,0.0007967094,0.001416491],"category_scores_gemma":[0.01262069,0.0006971877,0.000838547,0.00126555,0.0007615581,0.0006783611,0.001514331,0.0008949417,0.0004772029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003685032,"about_ca_system_score_gemma":0.005999849,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4490815,"about_ca_topic_score_gemma":0.5581306,"domain_scores_codex":[0.9988855,0.000430141,0.0001187847,0.0002973646,0.0001664249,0.0001017183],"domain_scores_gemma":[0.9944243,0.003407443,0.00035003,0.0003782467,0.001341957,0.00009798635],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0007330694,0.0006148891,0.3662654,0.0006440812,0.0007172042,0.0003276598,0.003005516,0.3716445,0.007795488,0.001728568,0.003003808,0.2435198],"study_design_scores_gemma":[0.00007281305,0.0001353366,0.09272875,0.0001121092,0.0001265205,0.00005063396,0.0007432042,0.8993537,0.003595558,0.0008390875,0.002184529,0.00005781146],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9262822,0.0007447809,0.06752401,0.0002227093,0.000025851,0.0005408944,0.001553741,0.0006810525,0.002424778],"genre_scores_gemma":[0.8935497,0.000557442,0.09699879,0.00009618585,0.00001506315,0.0009370733,0.004246192,0.0001904453,0.003409185],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5509186,"threshold_uncertainty_score":0.892935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04157262569985903,"score_gpt":0.2620866225360111,"score_spread":0.2205139968361521,"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."}}