{"id":"W2484314613","doi":"10.1016/b978-0-444-59424-2.00004-9","title":"Sample Collection and Management for Environmental Forensic Investigations","year":2013,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Soil and Land Suitability Analysis","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Documentation; Sample (material); Computer science; Risk analysis (engineering); Data science; Liability; Data collection; Quality (philosophy); Sampling (signal processing); Business; Statistics; Telecommunications; Accounting","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001241171,0.0009383204,0.0005342532,0.002370556,0.0009556304,0.001952311,0.001118051,0.000795025,0.05760679],"category_scores_gemma":[0.001466957,0.0004126034,0.0003340046,0.002179241,0.0006005209,0.001458058,0.0014035,0.000933396,0.0420039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006526284,"about_ca_system_score_gemma":0.002145563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006822178,"about_ca_topic_score_gemma":0.0308488,"domain_scores_codex":[0.9992298,0.00005583632,0.00006457901,0.0001409709,0.0004656696,0.00004323486],"domain_scores_gemma":[0.999401,0.0001431167,0.00004373001,0.00007968405,0.0002933178,0.00003918781],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004988072,0.00005455417,0.001997909,0.0003221981,0.00001057495,0.0002347896,0.0002155549,0.0003189635,0.0329617,0.001793426,0.05009069,0.9119498],"study_design_scores_gemma":[0.00000838352,0.00007867635,0.0109546,0.0003530484,0.00002674669,0.001102358,0.0004493763,0.0008315642,0.03539647,0.005488251,0.9452804,0.00003020225],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.03680565,0.02004407,0.3702346,0.002513715,0.002633277,0.001902905,0.01571695,0.005827742,0.5443211],"genre_scores_gemma":[0.02325367,0.01167097,0.2980839,0.001202098,0.0004008306,0.0007615472,0.008624166,0.001685039,0.6543178],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.05760679,"threshold_uncertainty_score":0.1927139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01240777254364437,"score_gpt":0.1971027434251266,"score_spread":0.1846949708814823,"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."}}