{"id":"W7115034241","doi":"","title":"Soil amendments for increasing water and nutrient use efficiencies, reducing fertilizer pollution, and greater green pepper yield and quality","year":2025,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Irrigation Practices and Water Management","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Yield (engineering); Nutrient; Fertilizer; Soil conditioner; Pepper; Crop yield; Soil nutrients","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0003971287,0.0006113864,0.0004041124,0.0004996069,0.0004406983,0.0009809013,0.00030333,0.0004474472,0.003229395],"category_scores_gemma":[0.0003489637,0.0002064125,0.0006610964,0.0005033661,0.0002462631,0.0004720226,0.0003051947,0.0004618878,0.0003968297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006314926,"about_ca_system_score_gemma":0.0009773769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002863087,"about_ca_topic_score_gemma":0.01110314,"domain_scores_codex":[0.9997584,0.00004000202,0.00002609197,0.0000664787,0.00006947899,0.00003948316],"domain_scores_gemma":[0.9997478,0.00005096763,0.00006822252,0.00001840422,0.00006916877,0.0000455279],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003645762,0.0001445111,0.001831513,0.000600193,0.00004781285,0.0001055485,0.000036861,0.0007252555,0.9836305,0.0001331264,0.000121296,0.0122588],"study_design_scores_gemma":[0.0000742936,0.002943515,0.02749111,0.0001328514,0.0002538463,0.00020179,0.0002454415,0.001614282,0.9444981,0.0002599269,0.02224489,0.00003988378],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9835804,0.005480226,0.005086708,0.000194579,0.0001405928,0.0001904436,0.0009414469,0.000146391,0.004239263],"genre_scores_gemma":[0.9771321,0.003726358,0.01162077,0.0001547736,0.00002584408,0.0001017946,0.0006032435,0.00003347539,0.006601585],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003229395,"threshold_uncertainty_score":0.01080346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05103171397947724,"score_gpt":0.2564170335590716,"score_spread":0.2053853195795943,"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."}}