{"id":"W2185573266","doi":"","title":"WEED COVER ON AND BETWEEN CORN ROWS IMPLICATIONS FOR REAL-TIME WEED DETECTION","year":2010,"lang":"en","type":"article","venue":"","topic":"Weed Control and Herbicide Applications","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Weed; Hectare; Agronomy; Weed control; Mathematics; Crop; Tillage; Cover crop; Canopy; Row; Biology; Agriculture; Computer science; Botany; Ecology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001477611,0.0002399681,0.000338461,0.0005007636,0.0004039091,0.00112529,0.0004138606,0.0003368409,0.001809194],"category_scores_gemma":[0.007996168,0.0001473803,0.0001973285,0.0007049314,0.0005119951,0.0005506247,0.0002780381,0.0002789989,0.0001797136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001643284,"about_ca_system_score_gemma":0.0009673494,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1157363,"about_ca_topic_score_gemma":0.1554535,"domain_scores_codex":[0.9990665,0.0002851133,0.00004906837,0.0002028048,0.0002930279,0.0001035447],"domain_scores_gemma":[0.9947707,0.002818527,0.001024039,0.0003011548,0.0008473729,0.0002381506],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008987486,0.0001019062,0.8208721,0.0002634258,0.00009774459,0.0003967379,0.0006274279,0.01032794,0.05939881,0.0005019854,0.001897406,0.1046158],"study_design_scores_gemma":[0.000006437311,0.00008156734,0.9832767,0.00003000111,0.00002834646,0.00009366576,0.0005427058,0.01280056,0.002159034,0.0002483562,0.0007180231,0.00001456248],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9864261,0.0007799161,0.007136765,0.0007208513,0.00004293038,0.00005114183,0.0007453695,0.000137199,0.003959796],"genre_scores_gemma":[0.9961014,0.0001142373,0.003198022,0.0000706702,0.00001205404,0.00001217268,0.00015908,0.00000988447,0.0003224061],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1157363,"threshold_uncertainty_score":0.2301254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01679962371694355,"score_gpt":0.2375675351810715,"score_spread":0.2207679114641279,"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."}}