{"id":"W3135661602","doi":"","title":"Tracing Food-Energy-Water-Material Interdependencies and Injustices in Transboundary Supply Chains Through Remote Sensing of Boreal Deforestation in Eeyou Istchee/Jamésie, Quebec","year":2020,"lang":"en","type":"article","venue":"AGU Fall Meeting Abstracts","topic":"Indigenous Studies and Ecology","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Deforestation (computer science); Supply chain; Food chain; Tracing; Environmental science; Geography; Natural resource economics; Remote sensing; Environmental resource management; Business; Ecology; Economics; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004052961,0.0002373142,0.0001889134,0.001066902,0.001305311,0.001312289,0.0005398853,0.0003538997,0.001556447],"category_scores_gemma":[0.001222012,0.0001803828,0.0001981519,0.002198782,0.000733854,0.000459866,0.0007794481,0.0003987669,0.0001959103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008960048,"about_ca_system_score_gemma":0.007067365,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9849326,"about_ca_topic_score_gemma":0.9940006,"domain_scores_codex":[0.9997367,0.00004474536,0.00001439886,0.0000520077,0.00006768763,0.00008437039],"domain_scores_gemma":[0.9992784,0.0001578017,0.0001179708,0.00003311178,0.0003507484,0.00006206482],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007753213,0.00008138939,0.9627395,0.0000752991,0.00009411741,0.0002674046,0.006401552,0.002953137,0.003922434,0.0005098644,0.001335977,0.02154187],"study_design_scores_gemma":[0.000003600787,0.00001038278,0.9871438,0.00004167693,0.00001516062,0.00002655604,0.008088585,0.002438567,0.0003062706,0.00006481136,0.001851075,0.000009483871],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950375,0.0001261858,0.0005939945,0.0001546506,0.000002855601,0.00003995519,0.001251721,0.000009472585,0.002783675],"genre_scores_gemma":[0.9967108,0.0001505206,0.0007933139,0.00003890016,0.000001748409,0.00002173182,0.0005952723,0.000006053273,0.001681666],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0150674,"threshold_uncertainty_score":0.06501007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03215336818727181,"score_gpt":0.3041115681563208,"score_spread":0.271958199969049,"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."}}