{"id":"W2068752834","doi":"10.1088/1748-9326/8/4/045002","title":"Land surface phenologies and seasonalities using cool earthlight in mid-latitude croplands","year":2013,"lang":"en","type":"article","venue":"Environmental Research Letters","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Environmental science; Phenology; Vegetation (pathology); VNIR; Growing degree-day; Normalized Difference Vegetation Index; Climatology; Atmospheric sciences; Seasonality; Latitude; Growing season; Enhanced vegetation index; Physical geography; Leaf area index; Remote sensing; Geography; Vegetation Index; Ecology; Geology","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":[0.0003904487,0.0001836995,0.0001630389,0.00004805331,0.0002203139,0.0001406681,0.0002169015,0.00009161751,0.001359316],"category_scores_gemma":[0.00004361778,0.0001455016,0.00002907647,0.0001712228,0.001055672,0.0003987849,0.0004837227,0.0004230072,0.0006897566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004203505,"about_ca_system_score_gemma":0.000004632796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00209458,"about_ca_topic_score_gemma":0.0001936763,"domain_scores_codex":[0.9977208,0.0002638929,0.0001768293,0.000445849,0.0007447139,0.0006479429],"domain_scores_gemma":[0.9993958,0.0001870367,0.00003567327,0.0002350509,0.000002046208,0.0001444325],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001339339,0.00004072615,0.5972671,0.000006678276,0.000008267366,0.00003868497,0.0005679716,0.004920903,0.3934783,0.000001328153,0.002894283,0.0007623907],"study_design_scores_gemma":[0.0003962452,0.00004027684,0.9866807,0.00002893869,0.000002977801,0.00003179521,0.0006400417,0.002450479,0.007358101,0.00008027769,0.002050888,0.000239227],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967067,0.0001938346,0.00001647207,0.00206424,0.0000428023,0.0003879625,0.000007383036,0.00002263494,0.0005579402],"genre_scores_gemma":[0.9963592,0.0001158462,0.002498369,0.000302929,0.00003491335,0.000003942012,0.00001095726,0.00001825408,0.0006555857],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3894137,"threshold_uncertainty_score":0.9995536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0262542022177421,"score_gpt":0.2619991697720042,"score_spread":0.2357449675542621,"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."}}