{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001184296,0.0004250132,0.0004219757,0.00007889314,0.001546633,0.0004167382,0.0001690347,0.0003149378,0.00005449836],"category_scores_gemma":[0.0003236666,0.0001982861,0.00009776569,0.0001621068,0.00006074045,0.0008228526,0.0002213846,0.0003131755,0.000003456247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001179936,"about_ca_system_score_gemma":0.000006242203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006179519,"about_ca_topic_score_gemma":0.002019249,"domain_scores_codex":[0.9974252,0.0002322609,0.0005783192,0.0009294434,0.000340281,0.0004944789],"domain_scores_gemma":[0.9988955,0.0003236246,0.0002616949,0.0001317125,0.0002050179,0.0001824096],"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":[0.002019552,0.0003148548,0.002404113,0.001238265,0.0003634579,0.0000120188,0.0002334437,0.000008215423,0.749267,0.01509781,0.00002139411,0.2290198],"study_design_scores_gemma":[0.003663988,0.001312178,0.397592,0.002359938,0.001431068,0.00004871167,0.004872435,0.0001854532,0.3486855,0.02189324,0.2139882,0.003967322],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964331,0.0001402611,1.555945e-7,0.0003348871,0.0002693986,0.0007984228,0.000537105,0.0000635115,0.001423201],"genre_scores_gemma":[0.988714,0.0001999694,0.000099371,0.0005310827,0.00003526808,0.00009179057,0.000778017,0.000006993605,0.009543539],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4005816,"threshold_uncertainty_score":0.9997532,"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."}}