{"id":"W4386846545","doi":"10.1080/07038992.2023.2256895","title":"Multi-Source Remote Sensing Based Modeling of Vegetation Productivity in the Boreal: Issues & Opportunities","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; Natural Resources Canada; Canadian Forest Service; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency","keywords":"Remote sensing; Environmental science; Vegetation (pathology); Land cover; Boreal; Productivity; Terrestrial ecosystem; Wetland; Ecosystem; Geospatial analysis; Geography; Land use; Ecology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001514348,0.0001274052,0.0002390352,0.0005277876,0.0001835713,0.0000800604,0.0001256551,0.00006388995,0.000018509],"category_scores_gemma":[0.0002774426,0.0001023487,0.00007948386,0.0004653431,0.00008929504,0.0001785889,0.000003162444,0.0002584857,0.000006921565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002133496,"about_ca_system_score_gemma":0.0004031466,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.2633784,"about_ca_topic_score_gemma":0.8312129,"domain_scores_codex":[0.998656,0.0002341041,0.000381979,0.0001341937,0.0002534713,0.0003401927],"domain_scores_gemma":[0.9990489,0.0001601819,0.0002169535,0.0001842482,0.0001958439,0.0001938778],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003430973,0.000002135056,0.004044892,0.0001151175,0.00001792354,0.0006426471,0.015103,0.1078258,0.0007837553,4.042008e-7,0.0003284998,0.8711015],"study_design_scores_gemma":[0.0001813295,0.00002833069,0.01326806,0.000392934,0.00001873241,0.0002344951,0.003583976,0.9781228,0.00007420805,0.0001307647,0.003845637,0.0001187586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9872544,0.0006592821,0.007858463,0.003335007,0.0003620712,0.0001067845,0.00005196155,0.00001029065,0.0003617525],"genre_scores_gemma":[0.9827296,0.0001152415,0.01640026,0.0002827162,0.0002609637,7.477793e-10,0.0001524998,0.000009201573,0.00004952613],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8709828,"threshold_uncertainty_score":0.7415268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1525247485862729,"score_gpt":0.283807022453732,"score_spread":0.1312822738674591,"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."}}