{"id":"W2080010866","doi":"10.1007/s11119-011-9233-6","title":"Site-specific early season potato yield forecast by neural network in Eastern Canada","year":2011,"lang":"en","type":"article","venue":"Precision Agriculture","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":65,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Growing season; Leaf area index; Yield (engineering); Mathematics; Sowing; Precision agriculture; Regression analysis; Variable (mathematics); Spatial variability; Artificial neural network; Agronomy; Environmental science; Statistics; Agriculture; Computer science; Ecology; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000291833,0.0003353393,0.0002871715,0.0005245263,0.000722886,0.0007061599,0.0007577524,0.0003486526,0.001244746],"category_scores_gemma":[0.0008302045,0.0001945241,0.0003096673,0.0009601544,0.0002347617,0.0002457267,0.0002091607,0.0003099272,0.0001938455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01172894,"about_ca_system_score_gemma":0.007739997,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9917634,"about_ca_topic_score_gemma":0.9942715,"domain_scores_codex":[0.9998727,0.000008389076,0.000005730098,0.00003554007,0.00003122635,0.00004647418],"domain_scores_gemma":[0.9993826,0.00007473501,0.00003182554,0.00001966145,0.0004156155,0.00007553957],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007956227,0.0002483315,0.514447,0.00009205479,0.0002209561,0.0004824329,0.0003128451,0.424708,0.004989791,0.0009789912,0.008360911,0.04436316],"study_design_scores_gemma":[0.0000595344,0.00003251838,0.4245726,0.00001587368,0.00006934384,0.0000294297,0.0006058759,0.5709785,0.001428948,0.0002294635,0.001927389,0.00005062414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959961,0.0001151758,0.0006033901,0.00009850365,0.000009616415,0.00000940307,0.001679556,0.00007920336,0.001408939],"genre_scores_gemma":[0.9966908,0.00006478856,0.0004566702,0.00001119916,0.000001953766,0.000002978953,0.001253224,0.000008690168,0.001509725],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01172894,"threshold_uncertainty_score":0.08509982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01473063037120616,"score_gpt":0.1772143551550727,"score_spread":0.1624837247838665,"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."}}