{"id":"W3149570175","doi":"","title":"Reconstruction of the past 1000-a temperature in Canada based on pollen data","year":2002,"lang":"en","type":"article","venue":"中国科学通报：英文版","topic":"Geology and Paleoclimatology Research","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Climate change; Pollen; Climatology; Natural (archaeology); China; Geography; Global warming; Physical geography; Scale (ratio); Global temperature; Forcing (mathematics); Geology; Cartography; Ecology; Archaeology; Oceanography; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002106259,0.00007740515,0.0001293238,0.00005926323,0.0001062698,0.000006886834,0.0004796704,0.00008924666,0.003847288],"category_scores_gemma":[0.00005557219,0.00005115858,0.00001773183,0.0002553287,0.0001175419,0.00006313443,0.00001850963,0.0003128055,0.00006375356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003952838,"about_ca_system_score_gemma":0.000220062,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5282626,"about_ca_topic_score_gemma":0.9876778,"domain_scores_codex":[0.999028,0.0001977389,0.0001497952,0.0002101395,0.0001814165,0.0002329611],"domain_scores_gemma":[0.9991904,0.0002023167,0.00004706586,0.0004948511,0.00001833077,0.00004703675],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002019346,0.000007158588,0.9938334,0.000009466078,0.000004163865,0.0000117634,0.00003829057,0.0007273435,0.000002902391,0.000007528436,0.003492582,0.001845272],"study_design_scores_gemma":[0.0002378458,0.00002947003,0.9840281,0.00002146255,0.000003411222,0.00003156679,0.0001935172,0.01430329,0.00004860801,0.00001530082,0.001025739,0.00006169346],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9744813,0.000228301,1.698756e-7,0.003189818,0.0004045745,0.0001061358,0.0001713759,0.000004657139,0.02141362],"genre_scores_gemma":[0.9990754,0.00002680159,0.00003184105,0.0003937838,0.0000351299,5.52204e-7,0.00007051095,0.000001354557,0.0003646734],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4594152,"threshold_uncertainty_score":0.9970633,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02513578635346076,"score_gpt":0.2048052986205132,"score_spread":0.1796695122670524,"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."}}