{"id":"W3031466865","doi":"","title":"ASSESSING REGIONAL AGROECOSYSTEM HEALTH COMBINING ECOLOGICAL-SOCIAL-ECONOMIC COMPONENTS USING CASE STUDIES OF NOVA SCOTIA AND FUJIAN PROVINCES","year":2019,"lang":"en","type":"article","venue":"","topic":"Agriculture and Biological Studies","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Agroecosystem; Nova scotia; Geography; Ecology; Agriculture; Biology; Archaeology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004170377,0.0002034593,0.0006236965,0.00001105933,0.0004403671,0.00006885223,0.0001212362,0.0001050625,0.00009995313],"category_scores_gemma":[0.0000159929,0.00006199969,0.00009360522,0.000123726,0.000175786,0.0001907945,0.000216234,0.0001034585,0.00001554832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001018253,"about_ca_system_score_gemma":0.00001149636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002378399,"about_ca_topic_score_gemma":0.005266678,"domain_scores_codex":[0.998538,0.0001727717,0.0004580487,0.0003894724,0.0001201204,0.0003216504],"domain_scores_gemma":[0.9990728,0.0003705766,0.0003817494,0.00003060529,0.00007075565,0.00007348101],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006726622,0.0004685676,0.822448,0.0003205394,0.0004476448,0.000116451,0.000873476,0.000021697,0.145679,0.002952891,0.001537855,0.02506661],"study_design_scores_gemma":[0.0004610435,0.001077053,0.9643188,0.0001693946,0.00003619855,0.0006954752,0.03090657,0.0002087401,0.0002910133,0.0002496665,0.001087524,0.0004985332],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965754,0.0008689148,8.851943e-7,0.001627518,0.0001577966,0.000375506,0.00001714137,0.00004474476,0.0003321239],"genre_scores_gemma":[0.9992601,0.00007722893,0.0001758936,0.0002885851,0.0001316895,0.00000232265,0.00002172195,7.427466e-7,0.00004172258],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.145388,"threshold_uncertainty_score":0.3595444,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.131947298089934,"score_gpt":0.3239953891366252,"score_spread":0.1920480910466912,"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."}}