{"id":"W3006138665","doi":"10.1002/ecs2.3036","title":"Equitable transform applied to phenology and temperature in a changing climate: Scaling to maintain individuality","year":2020,"lang":"en","type":"article","venue":"Ecosphere","topic":"Tree-ring climate responses","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; ArcticNet; Polar Knowledge Canada","keywords":"Missing data; Scaling; Set (abstract data type); Sequence (biology); Data set; Principal component analysis; Transformation (genetics); Function (biology); Computer science; Multidimensional scaling; Data mining; Algorithm; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005233286,0.0001619786,0.0002584345,0.00008498682,0.0001601058,0.00009917085,0.0001834813,0.000101119,0.001247541],"category_scores_gemma":[0.00006164792,0.0001502445,0.0000232209,0.0007477106,0.00002092874,0.0001066298,0.00005957097,0.0001899124,0.0005548911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001106001,"about_ca_system_score_gemma":0.00002986191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002086229,"about_ca_topic_score_gemma":0.004957788,"domain_scores_codex":[0.9983744,0.00005152755,0.0002173864,0.0003901901,0.00014053,0.0008259522],"domain_scores_gemma":[0.9994391,0.0001165421,0.00002524974,0.0001135313,0.00000905102,0.0002965468],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.003510513,0.0001081636,0.4059769,0.001087022,0.0001067352,0.0003550152,0.05081332,0.0516523,0.007257419,0.00752056,0.003652489,0.4679596],"study_design_scores_gemma":[0.001900623,0.0006766286,0.9530062,0.0002098427,0.0000321323,0.00003531194,0.01721933,0.003366848,0.002976341,0.0009655855,0.01846356,0.001147574],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9833581,0.0002528432,0.00007392332,0.004014101,0.00006124191,0.0004024448,0.0001599961,0.00008475831,0.01159256],"genre_scores_gemma":[0.9947852,0.00002707525,0.002647732,0.00237589,0.00007319041,0.000007052525,0.00003381071,0.000008129465,0.00004195025],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5470294,"threshold_uncertainty_score":0.9996654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01686606564880294,"score_gpt":0.2325583538700978,"score_spread":0.2156922882212949,"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."}}