{"id":"W4416408078","doi":"10.1101/2025.11.14.688354","title":"Increasing spatial approximation complexity can degrade prediction quality in distribution models","year":2025,"lang":"","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; Fisheries and Oceans Canada","funders":"","keywords":"Groundfish; Scale (ratio); Range (aeronautics); Spatial ecology; Population; Image resolution; Gaussian; Gaussian process; Mean squared error; Variance (accounting)","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":[0.04468183,0.001104066,0.001423472,0.001245431,0.001237787,0.00261182,0.00216884,0.001839273,0.001252991],"category_scores_gemma":[0.2082836,0.001318749,0.001624754,0.001022508,0.001793124,0.004231021,0.002725725,0.002692567,0.0002912373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001848389,"about_ca_system_score_gemma":0.001615547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03006014,"about_ca_topic_score_gemma":0.02078667,"domain_scores_codex":[0.9803743,0.01302621,0.002312684,0.002075422,0.001614882,0.0005964555],"domain_scores_gemma":[0.6351587,0.3255752,0.01137303,0.0184634,0.008125813,0.001303921],"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":[0.0006848709,0.00009643396,0.1055893,0.0001571355,0.0005781283,0.0002052214,0.0005277802,0.8642588,0.001022251,0.002472702,0.001073548,0.02333389],"study_design_scores_gemma":[0.00005574033,0.0001168891,0.00911954,0.00008437184,0.00008458832,0.0000861273,0.0001781924,0.9839895,0.001072785,0.004760318,0.0004000186,0.00005197736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7191616,0.00134363,0.272249,0.002431128,0.0001544247,0.0001680682,0.0007568685,0.001704835,0.002030447],"genre_scores_gemma":[0.9753073,0.0001694872,0.02348353,0.0002911623,0.00003237964,0.00004609204,0.0003581317,0.0001176078,0.0001943399],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04468183,"threshold_uncertainty_score":0.2363029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05073821264409075,"score_gpt":0.2559076881514428,"score_spread":0.205169475507352,"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."}}