{"id":"W4250998482","doi":"10.5194/gmdd-8-979-2015","title":"The Polar Vegetation Photosynthesis and Respiration Model (PolarVPRM): a parsimonious, satellite data-driven model of high-latitude CO <sub>2</sub> exchange","year":2015,"lang":"en","type":"preprint","venue":"","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Tundra; Eddy covariance; Environmental science; Vegetation (pathology); Atmospheric sciences; Latitude; Photosynthesis; Ecosystem respiration; Growing season; Primary production; Arctic; Ecosystem; Climatology; Ecology; Geography; Botany; Biology; Geology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003432166,0.0006520368,0.0003763432,0.0002360723,0.0003206661,0.0007308933,0.001022883,0.0006721811,0.001375728],"category_scores_gemma":[0.000665524,0.0004359216,0.0005149566,0.0004683223,0.0002402401,0.0007227482,0.0005269852,0.0005340378,0.0004345723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005228198,"about_ca_system_score_gemma":0.0009278476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01598104,"about_ca_topic_score_gemma":0.01442687,"domain_scores_codex":[0.999907,0.00003248353,0.000003811531,0.00003209315,0.00001553869,0.000009089797],"domain_scores_gemma":[0.9998434,0.00006053989,0.00002902689,0.00001493987,0.00003405242,0.00001791263],"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":[0.00004386846,0.00001657383,0.00325631,0.00003133574,0.00004555742,0.00004723156,0.00001616973,0.9858919,0.0009765864,0.002105525,0.001439078,0.006129953],"study_design_scores_gemma":[0.00001343387,0.00000697786,0.0008114892,0.00000474231,0.000008585214,0.00001493524,0.000004644237,0.9970111,0.0001886235,0.0009928094,0.0009372578,0.000005337899],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4417587,0.001769197,0.5225496,0.001464525,0.0003093741,0.0001188929,0.008248812,0.003241765,0.02053911],"genre_scores_gemma":[0.9350007,0.0007228553,0.0570969,0.0001656954,0.00009402511,0.0001678588,0.003212701,0.0002619924,0.003277239],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01598104,"threshold_uncertainty_score":0.03177607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1018945828688511,"score_gpt":0.2732488520773542,"score_spread":0.1713542692085031,"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."}}