{"id":"W2904746169","doi":"10.2495/sdp180301","title":"ADOPTION OF PRECISION AGRICULTURE TO REDUCE INPUTS, ENHANCE SUSTAINABILTIY AND INCREASE FOOD PRODUCTION: A STUDY OF SOUTHERN ALBERTA, CANADA","year":2018,"lang":"en","type":"article","venue":"WIT transactions on ecology and the environment","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Production (economics); Agriculture; Precision agriculture; Food processing; Environmental science; Agricultural economics; Agricultural engineering; Geography; Economics; Engineering; Food science; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006802846,0.000442091,0.000354688,0.001088998,0.005397003,0.001663826,0.0010217,0.0004582323,0.001533082],"category_scores_gemma":[0.0009772978,0.0003420232,0.0003627731,0.003404751,0.001210663,0.0003402381,0.0007117128,0.0007063663,0.0002172488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03945592,"about_ca_system_score_gemma":0.05187189,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9970973,"about_ca_topic_score_gemma":0.9991803,"domain_scores_codex":[0.9993351,0.00005749697,0.0000184778,0.00008853566,0.0002140507,0.0002862756],"domain_scores_gemma":[0.9988949,0.0001021117,0.0001218655,0.00002831017,0.0005183342,0.0003345618],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005259876,0.0007717457,0.8958729,0.0001910191,0.00009478468,0.002078673,0.04922564,0.0007044502,0.004602232,0.001389028,0.003388291,0.04115518],"study_design_scores_gemma":[0.00003428432,0.0002048338,0.9327449,0.00007886308,0.00003858368,0.0001636181,0.05997777,0.0008369455,0.0002636374,0.0001060605,0.00551276,0.00003766825],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972909,0.0002128146,0.0000702031,0.0001694069,0.000003601812,0.00003617101,0.0002438973,0.000003562923,0.001969495],"genre_scores_gemma":[0.9941439,0.0005866156,0.0002880136,0.0002072724,0.000002654455,0.00002305669,0.0003094536,0.000006114386,0.004433071],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03945592,"threshold_uncertainty_score":0.2862741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004143438824335112,"score_gpt":0.1718487653762118,"score_spread":0.1677053265518767,"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."}}