{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001457346,0.0001013999,0.0001562598,0.000008607879,0.000245745,0.000004694039,0.00007592075,0.00005661571,0.00007705957],"category_scores_gemma":[0.00001511374,0.00003363433,0.00002278311,0.0001134103,0.0001454202,0.00003027037,0.00001105173,0.00008240647,0.000003036133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002815307,"about_ca_system_score_gemma":0.00000806909,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.03817388,"about_ca_topic_score_gemma":0.5257313,"domain_scores_codex":[0.9992529,0.0001200137,0.0001690745,0.0002310933,0.000116296,0.0001106555],"domain_scores_gemma":[0.999652,0.0001264968,0.00007536483,0.00006624017,0.00002857618,0.00005128288],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.01481383,0.01800143,0.06761816,0.000180956,0.001906755,0.00001295973,0.06594101,0.00501135,0.4629176,0.0006413823,0.003780404,0.3591742],"study_design_scores_gemma":[0.001400613,0.008668907,0.9022695,0.00004100294,0.0002409974,0.00002634828,0.02755082,0.000009462781,0.05119304,0.0002524462,0.008012248,0.0003346265],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.995853,0.00003886393,0.00001792518,0.003318326,0.00007341793,0.0006405516,0.00001637789,0.000003835567,0.00003770045],"genre_scores_gemma":[0.9989918,0.0000303078,0.00001423589,0.00008364871,0.00006341688,0.00006589382,0.000003724162,6.74774e-7,0.000746327],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8346514,"threshold_uncertainty_score":0.968231,"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."}}