{"id":"W4410706944","doi":"10.1016/j.isprsjprs.2025.05.022","title":"Aboveground biomass mapping of Canada with SAR and optical satellite observations aided by active learning","year":2025,"lang":"en","type":"article","venue":"ISPRS Journal of Photogrammetry and Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Forest Service; Natural Resources Canada; McMaster University","funders":"Alliance de recherche numérique du Canada; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; China Scholarship Council; McMaster University","keywords":"Remote sensing; Satellite; Environmental science; Biomass (ecology); Satellite imagery; Geography; Geology; Engineering; Oceanography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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":[0.0002751644,0.0001317772,0.0002516217,0.00009089558,0.0002611243,0.00004631274,0.00006229759,0.00006510455,0.000002802823],"category_scores_gemma":[0.00008804644,0.0001063842,0.00003698027,0.0005500707,0.0002711832,0.0001009796,0.0000457622,0.0002985031,1.348695e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001059109,"about_ca_system_score_gemma":0.00008548036,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.08568247,"about_ca_topic_score_gemma":0.02078572,"domain_scores_codex":[0.99897,0.00006572005,0.000339767,0.0001753489,0.000240492,0.0002086909],"domain_scores_gemma":[0.9992281,0.0002220548,0.0002623465,0.0001040406,0.00005775593,0.0001256895],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008060788,0.00002192106,0.002651352,0.00003706959,0.0001099646,0.00002530605,0.0004778161,0.0001815569,0.3105821,0.00001902726,0.0001761621,0.6856372],"study_design_scores_gemma":[0.004386823,0.0007028591,0.2217266,0.002277552,0.000614345,0.002195258,0.01800874,0.07790712,0.4972827,0.002406905,0.1711535,0.001337614],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9379762,0.0003108732,0.05829734,0.0005880524,0.00005778332,0.00009419787,0.00000183924,0.000006881678,0.002666855],"genre_scores_gemma":[0.9615219,0.0001392055,0.03800135,0.0001180796,0.00001615892,6.417705e-9,0.000001831281,0.000009089871,0.0001923399],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6842995,"threshold_uncertainty_score":0.9970824,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008661676518025584,"score_gpt":0.2171901870825269,"score_spread":0.2085285105645013,"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."}}