{"id":"W3105233041","doi":"","title":"The Value of Agricultural Data in Carbon Cycle Studies: A Case Study from Ontario","year":2009,"lang":"en","type":"article","venue":"AGU Spring Meeting Abstracts","topic":"Indigenous Studies and Ecology","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Agriculture; Value (mathematics); Agricultural economics; Environmental science; Economics; Geography; Statistics; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004699369,0.0002493882,0.0002898865,0.001321878,0.009361615,0.003500613,0.0009781721,0.001332511,0.001751069],"category_scores_gemma":[0.0150174,0.0002903751,0.000241337,0.005253272,0.003093326,0.001386008,0.001720301,0.0008897489,0.0001011843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04191579,"about_ca_system_score_gemma":0.04145971,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9709009,"about_ca_topic_score_gemma":0.992474,"domain_scores_codex":[0.9972569,0.001119716,0.0001326554,0.0001421378,0.0007265012,0.000622131],"domain_scores_gemma":[0.9804551,0.01345954,0.0009253606,0.0006783208,0.003584702,0.0008969817],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005244531,0.0002868115,0.4674797,0.0008903199,0.0001584922,0.02694624,0.3848068,0.004458455,0.003795953,0.01833238,0.00796392,0.08435643],"study_design_scores_gemma":[0.00006674327,0.0002154014,0.339313,0.0006330723,0.0001999492,0.002882188,0.5302748,0.00471289,0.002166559,0.003568771,0.1158439,0.0001226789],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9747766,0.0007626576,0.0004982349,0.003290097,0.00001102562,0.00008303361,0.0003668992,0.00001109754,0.02020027],"genre_scores_gemma":[0.9955233,0.0008102284,0.001090499,0.0001372782,0.000006168003,0.00001936037,0.0001082407,0.00001186647,0.002293017],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04191579,"threshold_uncertainty_score":0.3041217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07367453699257547,"score_gpt":0.3805524939688103,"score_spread":0.3068779569762348,"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."}}