{"id":"W3194221996","doi":"10.1080/07055900.2021.1947181","title":"Reconstructing the Atlantic Overturning Circulation Using Linear Machine Learning Techniques","year":2021,"lang":"en","type":"article","venue":"ATMOSPHERE-OCEAN","topic":"Climate variability and models","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Climate Program Office; National Oceanic and Atmospheric Administration","keywords":"Interpretability; Machine learning; Algorithm; Artificial intelligence; Climatology; Computer science; Scale (ratio); Geology; Mathematics; Geography; Cartography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.0006925734,0.000477001,0.0003224303,0.0005607292,0.0002158488,0.0006235874,0.0002975474,0.0003459767,0.0005342359],"category_scores_gemma":[0.003128215,0.000299191,0.0003520484,0.0004219651,0.0003428003,0.0006827172,0.0003293438,0.0006384812,0.0001393132],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005057628,"about_ca_system_score_gemma":0.0007331724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01122407,"about_ca_topic_score_gemma":0.009640565,"domain_scores_codex":[0.9998343,0.00007419918,0.000009735924,0.00003424314,0.00002717036,0.00002033624],"domain_scores_gemma":[0.9989128,0.0007753967,0.0001000274,0.00007950843,0.0001084138,0.00002393714],"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":[0.00003923484,0.00002467916,0.005660984,0.00002028992,0.00002808151,0.00003050849,0.00003208729,0.9431646,0.002418736,0.001702953,0.000189089,0.04668872],"study_design_scores_gemma":[0.000001472599,0.000003308227,0.0002736125,7.06825e-7,7.598994e-7,0.000001126033,0.000002379702,0.9989064,0.0002268223,0.000551509,0.00003052337,0.00000133785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3650556,0.0002521081,0.632303,0.0003142754,0.00002888311,0.00001842988,0.000151327,0.0008098279,0.001066625],"genre_scores_gemma":[0.904397,0.00009880006,0.0945926,0.00002988289,0.00001903835,0.00001911061,0.000190979,0.00003266375,0.0006198936],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01122407,"threshold_uncertainty_score":0.02231747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02117110959344223,"score_gpt":0.244870176336233,"score_spread":0.2236990667427908,"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."}}