{"id":"W2903713900","doi":"10.1007/s41064-018-0059-y","title":"Proxy Indicators for Mapping the End of the Vegetation Active Period in Boreal Forests Inferred from Satellite-Observed Soil Freeze and ERA-Interim Reanalysis Air Temperature","year":2018,"lang":"en","type":"article","venue":"PFG – Journal of Photogrammetry Remote Sensing and Geoinformation Science","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Office of Science; Suomen Ympäristökeskus; European Commission; Academy of Finland; European Space Agency","keywords":"Environmental science; Evergreen; Taiga; Vegetation (pathology); Atmospheric sciences; Satellite; Water content; Climatology; Eddy covariance; Ecosystem; Geography; Ecology; Forestry; Geology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000454713,0.0003508331,0.0002115063,0.001137699,0.0003442495,0.0007793954,0.0003371252,0.0002751096,0.0004177667],"category_scores_gemma":[0.0007631192,0.000155398,0.0002522281,0.0009970773,0.0001963533,0.0003727837,0.0003372116,0.0002102057,0.00009861704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006924886,"about_ca_system_score_gemma":0.0007381953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1486066,"about_ca_topic_score_gemma":0.2782726,"domain_scores_codex":[0.9998375,0.00002257886,0.00001117411,0.00005462066,0.00003107443,0.00004289687],"domain_scores_gemma":[0.9995534,0.00008608458,0.0001261841,0.0000312738,0.0001308446,0.00007220979],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002192061,0.00006349623,0.9648709,0.00005601012,0.00007803097,0.00009011339,0.0002449858,0.00559992,0.01142886,0.00009751938,0.0002765009,0.01697437],"study_design_scores_gemma":[0.000005024234,0.00001600512,0.991816,0.000008011768,0.00001908071,0.00003024313,0.0001606702,0.007008275,0.0006802704,0.00002365201,0.0002243107,0.000008572516],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966977,0.0002036022,0.001424051,0.00001012076,0.000003737115,0.00001045947,0.001019203,0.00005195147,0.0005794019],"genre_scores_gemma":[0.9960623,0.00004845077,0.002453275,0.000004203067,0.000002320979,0.000008466471,0.001308814,0.000007107637,0.0001049371],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1486066,"threshold_uncertainty_score":0.2954832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01493136320143912,"score_gpt":0.2411401853788737,"score_spread":0.2262088221774346,"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."}}