{"id":"W2038448037","doi":"10.1371/journal.pone.0112894","title":"Applications of Low Altitude Remote Sensing in Agriculture upon Farmers' Requests– A Case Study in Northeastern Ontario, Canada","year":2014,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":94,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nipissing University; Algoma University","funders":"Northern Ontario Heritage Fund Corporation","keywords":"Agriculture; Remote sensing; Process (computing); Precision agriculture; Computer science; Tile drainage; Geographic information system; Altitude (triangle); Field (mathematics); Business; Agricultural engineering; Geography; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002079379,0.0001680794,0.000282258,0.00004044555,0.00005744499,0.0000145245,0.0001498445,0.00007684092,0.00003471787],"category_scores_gemma":[0.00003457242,0.0001361355,0.00002266074,0.0004383477,0.00003611293,0.00007194665,0.00009770366,0.0003176573,0.00001256405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009200449,"about_ca_system_score_gemma":0.00005354351,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9916125,"about_ca_topic_score_gemma":0.9998279,"domain_scores_codex":[0.9984111,0.0001266449,0.0003462445,0.0003779768,0.000470865,0.0002671739],"domain_scores_gemma":[0.9993472,0.00005051178,0.000130544,0.0003568993,0.00002369791,0.00009117638],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003753415,0.003350545,0.8769469,0.0001263146,0.00009969345,0.001833799,0.01268865,0.008274374,0.06887147,0.000002864009,0.0002041446,0.02756373],"study_design_scores_gemma":[0.002066404,0.0002913498,0.963305,0.000736581,0.0001520687,0.0006128838,0.006816451,0.0182097,0.005796661,0.00009679895,0.0009023727,0.001013706],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968944,0.000006235985,0.0001199268,0.0001393378,0.00001950311,0.0008544846,0.000002759874,0.00001589817,0.001947438],"genre_scores_gemma":[0.9948493,0.000001431069,0.004672667,0.00007816674,0.00002909398,0.000001312969,0.000008331563,0.00001091984,0.0003487562],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08635815,"threshold_uncertainty_score":0.5551447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01006139425510918,"score_gpt":0.193219965061337,"score_spread":0.1831585708062279,"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."}}