{"id":"W4200177682","doi":"10.3390/su14010040","title":"A Novel Feature Selection Technique to Better Predict Climate Change Stage of Change","year":2021,"lang":"en","type":"article","venue":"Sustainability","topic":"Environmental Education and Sustainability","field":"Environmental Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; Université Laval; Polytechnique Montréal","funders":"","keywords":"Overfitting; Feature selection; Random forest; Artificial intelligence; Machine learning; Elastic net regularization; Computer science; Selection (genetic algorithm); Feature (linguistics); Linear discriminant analysis; Lasso (programming language); Quadratic classifier; Principal component analysis; Climate change; Data mining; Pattern recognition (psychology); Support vector machine; Artificial neural network; Ecology","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007235244,0.0002121217,0.000243821,0.00005464028,0.000152202,0.00002108402,0.0001992577,0.000160613,0.002162208],"category_scores_gemma":[0.0004660329,0.000213089,0.0001138215,0.0007418443,0.0002393518,0.0004612973,0.0004915195,0.0002471788,0.00002328148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002408395,"about_ca_system_score_gemma":0.00008250313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008850265,"about_ca_topic_score_gemma":0.0003302337,"domain_scores_codex":[0.9979946,0.0001595471,0.0002829422,0.0006628899,0.0003731138,0.0005269351],"domain_scores_gemma":[0.9989178,0.00004058803,0.0000984841,0.0006087992,0.0001221652,0.0002121393],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007716848,0.001113177,0.9629852,0.0005430906,0.000008575506,0.000009159791,0.003591463,0.00004832356,0.01415313,0.0003887738,0.0005058051,0.01657615],"study_design_scores_gemma":[0.0002200723,0.0001440356,0.9552186,0.00001254693,0.0000121224,0.000009628356,0.002547216,0.00006746871,0.01275052,0.0008167242,0.02796305,0.000238028],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9816599,0.0000290042,0.00223068,0.01199854,0.0001115685,0.002730795,0.00009392354,0.00008996893,0.00105555],"genre_scores_gemma":[0.9909032,0.00001039297,0.00458293,0.001449637,0.00007862738,0.001283324,0.00002348575,0.00002275015,0.001645677],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02745724,"threshold_uncertainty_score":0.99875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01757269991873647,"score_gpt":0.2795650417065442,"score_spread":0.2619923417878078,"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."}}