{"id":"W1605875024","doi":"10.1111/j.1471-8286.2006.01428.x","title":"An inexpensive, automation‐friendly protocol for recovering high‐quality DNA","year":2006,"lang":"en","type":"article","venue":"Molecular Ecology Notes","topic":"Identification and Quantification in Food","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1476,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Ontario Genomics Institute; Genome Canada; Gordon and Betty Moore Foundation","keywords":"Automation; Protocol (science); DNA extraction; Computer science; Embedded system; Biology; Engineering; Medicine; Polymerase chain reaction; Pathology; Genetics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.001370188,0.001125032,0.0005617696,0.002137865,0.001005631,0.0005309124,0.001217819,0.0009107513,0.0086482],"category_scores_gemma":[0.001966527,0.0008405609,0.0007296045,0.0009873102,0.0009242786,0.0006645619,0.0009156813,0.001642123,0.01054237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001920604,"about_ca_system_score_gemma":0.0009968284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004254729,"about_ca_topic_score_gemma":0.001619734,"domain_scores_codex":[0.9984897,0.0003450846,0.0002066737,0.0002730566,0.0005786617,0.0001067174],"domain_scores_gemma":[0.998724,0.000442997,0.0001221738,0.0002657826,0.0003443592,0.0001006939],"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":[0.00002841165,0.00004465149,0.0001733089,0.000227506,0.00001410145,0.0001191434,0.0000571506,0.00006657501,0.9881353,0.0002432696,0.00131922,0.009571281],"study_design_scores_gemma":[0.0001175018,0.0006786741,0.007780953,0.0001585976,0.0001426592,0.003284077,0.0001137433,0.001933937,0.9063863,0.001517431,0.077765,0.0001211505],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03731084,0.001632342,0.9472025,0.0003860152,0.0005752644,0.002752064,0.001803042,0.002146223,0.00619181],"genre_scores_gemma":[0.07093843,0.00249249,0.8978175,0.0006153598,0.0002582327,0.004639762,0.008141059,0.0005791202,0.01451799],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0086482,"threshold_uncertainty_score":0.02893108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02193901611322532,"score_gpt":0.3417341605356232,"score_spread":0.3197951444223979,"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."}}