{"id":"W2612061294","doi":"10.1080/24694452.2017.1309964","title":"Global Spatial–Temporal Variability in Terrestrial Productivity and Phenology Regimes between 2000 and 2012","year":2017,"lang":"en","type":"article","venue":"Annals of the American Association of Geographers","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service; University of British Columbia; University of Victoria","funders":"University of British Columbia","keywords":"Tundra; Environmental science; Phenology; Spatial variability; Spatial ecology; Photosynthetically active radiation; Shrubland; Common spatial pattern; Physical geography; Ecosystem; Vegetation (pathology); Seasonality; Primary production; Productivity; Climatology; Geography; Ecology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004924642,0.0002348629,0.0001721728,0.001518092,0.0001363097,0.0004318002,0.0001792599,0.0001813576,0.0006347032],"category_scores_gemma":[0.0009084023,0.0001164237,0.0003256699,0.002317631,0.0001766366,0.0004008584,0.0003842113,0.0001793063,0.0001385653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000570534,"about_ca_system_score_gemma":0.0002408563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0352319,"about_ca_topic_score_gemma":0.05227484,"domain_scores_codex":[0.9998363,0.00002015571,0.00001928791,0.00006730101,0.00002653707,0.00003036067],"domain_scores_gemma":[0.9992886,0.00009401592,0.0003079023,0.00006262912,0.0001890613,0.00005776569],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001133985,0.0000263485,0.9830728,0.00004454783,0.0001256068,0.00004950134,0.0003047429,0.001026978,0.001067964,0.000105943,0.001287459,0.0127748],"study_design_scores_gemma":[0.000001053777,0.000005857041,0.9990047,0.000003000715,0.00001082364,0.00001426175,0.00005010043,0.00038348,0.00006307093,0.00001608711,0.0004452089,0.000002489812],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9905071,0.0002248072,0.0003562857,0.0001027608,0.000009100622,0.000005474287,0.007697123,0.00004824818,0.001049086],"genre_scores_gemma":[0.9909015,0.0001373242,0.0004353329,0.00003503386,0.00001365365,0.00001512019,0.008017236,0.000009297614,0.0004355241],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0352319,"threshold_uncertainty_score":0.07005364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01422595206860334,"score_gpt":0.2708814910198699,"score_spread":0.2566555389512666,"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."}}