{"id":"W1491237327","doi":"10.1111/gean.12019","title":"Developing Spatial Weight Matrices for Incorporation into Multiple Linear Regression Models: An Example Using Grizzly Bear Body Size and Environmental Predictor Variables","year":2013,"lang":"en","type":"article","venue":"Geographical Analysis","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; University of Calgary","funders":"Ministry of Advanced Education, Government of Alberta","keywords":"Autoregressive model; Statistics; Linear regression; Mathematics; Regression analysis; Regression; Sampling (signal processing); Spatial analysis; Econometrics; Geography; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.01117838,0.001225769,0.0007327669,0.001179658,0.0005491446,0.001219631,0.001306194,0.0007724845,0.001989379],"category_scores_gemma":[0.03311375,0.0008428507,0.001290854,0.001440638,0.0003743402,0.001473718,0.001417833,0.001421707,0.0005775757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006392776,"about_ca_system_score_gemma":0.001704966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0257885,"about_ca_topic_score_gemma":0.04387989,"domain_scores_codex":[0.9963689,0.002804894,0.0001587099,0.0002984843,0.0002787529,0.00009029228],"domain_scores_gemma":[0.9811755,0.01559064,0.0009017739,0.0007997004,0.001406495,0.0001257926],"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":[0.0001498957,0.0003221108,0.02476388,0.0002203673,0.0005184241,0.0003346085,0.0007210364,0.7121668,0.002748091,0.02782479,0.001266235,0.2289637],"study_design_scores_gemma":[0.00002715566,0.0001012471,0.001654465,0.00004156411,0.00004491174,0.0000482348,0.0001052471,0.9874778,0.000820985,0.007930688,0.001714639,0.00003312428],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03892884,0.000115234,0.9594506,0.0001707208,0.00002806225,0.00009004,0.00008485065,0.0005550249,0.0005766214],"genre_scores_gemma":[0.2068308,0.0002393313,0.7911987,0.00005407121,0.00002798842,0.0002238,0.0002108934,0.000197407,0.001016835],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0257885,"threshold_uncertainty_score":0.05911767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03920399311577492,"score_gpt":0.2231550714195903,"score_spread":0.1839510783038154,"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."}}