{"id":"W4288721147","doi":"10.46647/ijetms.2022.v06i04.0066","title":"Automated Irrigation and Fencing using IOT","year":2022,"lang":"en","type":"article","venue":"International Journal of Engineering Technology and Management Sciences","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bell (Canada)","funders":"","keywords":"Fencing; Automation; Computer science; Arduino; Internet of Things; Computer security; Engineering; Embedded system","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.0001759813,0.0003935908,0.0004359993,0.0005568301,0.0005075067,0.0009489788,0.0006703314,0.0006333312,0.0031805],"category_scores_gemma":[0.0003856389,0.0001972187,0.000393388,0.0005741253,0.000234097,0.001360179,0.0007900971,0.0003227231,0.0009894152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002264076,"about_ca_system_score_gemma":0.0003548052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001166549,"about_ca_topic_score_gemma":0.001239459,"domain_scores_codex":[0.9996773,0.00003432974,0.00002578671,0.00007601328,0.000138307,0.00004829252],"domain_scores_gemma":[0.999793,0.00003784333,0.0000305981,0.00004749036,0.00007076826,0.00002024839],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004256865,0.0003006841,0.01280847,0.001206617,0.0001310732,0.001896605,0.0006543956,0.04192821,0.1574613,0.01747165,0.04189879,0.7238165],"study_design_scores_gemma":[0.0001060329,0.0005940998,0.01849508,0.0004591101,0.0002315443,0.001858442,0.001241405,0.4929897,0.110264,0.03406889,0.3394574,0.0002343642],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1015638,0.002886444,0.7468202,0.001797895,0.00102442,0.0005521764,0.001444072,0.01589265,0.1280184],"genre_scores_gemma":[0.8814087,0.001672426,0.09250354,0.0006461996,0.000124386,0.0002648338,0.0009049583,0.0001800319,0.02229493],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0031805,"threshold_uncertainty_score":0.01063985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01037200263567179,"score_gpt":0.2193758268734133,"score_spread":0.2090038242377415,"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."}}