{"id":"W2017012156","doi":"10.1016/s0304-3800(03)00267-9","title":"A remote sensing-based primary production model for grassland biomes","year":2003,"lang":"en","type":"article","venue":"Ecological Modelling","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":139,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Swedish National Space Agency; Styrelsen för Internationellt Utvecklingssamarbete","keywords":"Environmental science; Biome; Primary production; Grassland; Normalized Difference Vegetation Index; Leaf area index; Transpiration; Vegetation (pathology); Rangeland; Cloud cover; Land cover; Interception; Remote sensing; Atmospheric sciences; Computer science; Land use; Agroforestry; Geography; Ecology; Photosynthesis; Ecosystem","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":[],"consensus_categories":[],"category_scores_codex":[0.0003887298,0.0001819428,0.0001955006,0.00002600125,0.0002386067,0.00003376768,0.00009279243,0.0001750923,0.00003626716],"category_scores_gemma":[0.0001413879,0.0001293394,0.00010074,0.000193651,0.0001086519,0.00008581899,0.00002739123,0.0001489323,0.00005043578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002356925,"about_ca_system_score_gemma":0.00001585226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001320835,"about_ca_topic_score_gemma":0.00002756691,"domain_scores_codex":[0.9985968,0.00006777817,0.0002192664,0.0005349732,0.0002119259,0.0003692728],"domain_scores_gemma":[0.9994997,0.00009433585,0.00008707036,0.000208089,0.00002212878,0.00008865134],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001883542,0.00004674257,0.00006664784,0.000008596755,0.0000038996,0.000002632623,0.0000376916,0.9886596,0.006696097,0.00002470882,0.001743958,0.002690619],"study_design_scores_gemma":[0.000214716,0.00005701371,0.0002155398,0.000008634403,0.00001530975,0.00001136438,0.0000051878,0.9827034,0.001878927,0.01251897,0.002163184,0.000207741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2887598,0.00001407199,0.7075411,0.0004774333,0.0001569116,0.0003969533,0.00000138914,0.00009339653,0.002558923],"genre_scores_gemma":[0.5855891,0.000006206672,0.4132427,0.0003294065,0.00004013116,6.582861e-7,0.000009035115,0.0000122251,0.0007706015],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.2968293,"threshold_uncertainty_score":0.5274307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02940771137283908,"score_gpt":0.2215584045810104,"score_spread":0.1921506932081714,"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."}}