{"id":"W6948863812","doi":"10.5281/zenodo.11557204","title":"Interpretação de Interações entre Vegetação e Clima com IA Explanável","year":2024,"lang":"pt","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Interpretability; Process (computing); Vegetation (pathology); Information system; Data collection","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":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004652985,0.0003287671,0.000270612,0.0002703816,0.001419897,0.002430328,0.0009481189,0.0001157104,0.04343833],"category_scores_gemma":[0.0000490991,0.0003434973,0.0002219655,0.0005228342,0.0001643685,0.0004180041,0.001171262,0.0008892784,0.01896893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002294135,"about_ca_system_score_gemma":0.00001441542,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001753417,"about_ca_topic_score_gemma":1.326605e-7,"domain_scores_codex":[0.9973609,0.0004925277,0.000429755,0.0007060776,0.0003242545,0.0006864692],"domain_scores_gemma":[0.9987644,0.00005029435,0.0001020004,0.0004763827,0.0002376399,0.0003692921],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001423244,0.0002654911,0.00001110198,0.0002555545,0.0003234756,0.0000502302,0.007906917,0.0004307663,0.002427759,0.01528239,0.3822696,0.5906344],"study_design_scores_gemma":[0.0003640237,0.0002230912,0.00002498382,0.0004422039,0.00006854602,0.0001089373,0.001718867,0.1127636,0.0006051008,0.0003747312,0.8829591,0.0003468747],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05594386,0.004800235,0.5936261,0.01327144,0.005701566,0.002459246,0.003004977,0.004714628,0.3164779],"genre_scores_gemma":[0.9851683,0.0002198212,0.00005194598,0.0001773649,0.000612435,1.124295e-7,0.001775259,0.001516627,0.01047817],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9292244,"threshold_uncertainty_score":0.9999017,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03473094719135498,"score_gpt":0.2809978821863338,"score_spread":0.2462669349949788,"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."}}