{"id":"W4200276890","doi":"10.1016/j.dib.2021.107677","title":"Data set showing the development of a hyperspectral imaging technique using LA-ICP-MS to determine the spatial distribution of nutrients in soil cores","year":2021,"lang":"en","type":"article","venue":"Data in Brief","topic":"Geology and Paleoclimatology Research","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; University of Calgary; University of Guelph; Government of Newfoundland and Labrador; Canadian Forest Service; Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Research and Development Corporation of Newfoundland and Labrador; Department of Fisheries and Land Resources; Grain Research and Development Corporation","keywords":"Hyperspectral imaging; Spatial distribution; Environmental science; Nutrient; Data set; Biogeochemistry; Remote sensing; Rhizosphere; Soil science; Computer science; Environmental chemistry; Chemistry; Geography; Ecology; Geology; Biology; Artificial intelligence","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":[0.0003033755,0.0008237486,0.0004399975,0.002258813,0.0006543192,0.0004955537,0.0006249231,0.0005295125,0.01524844],"category_scores_gemma":[0.0005311187,0.0002743113,0.0004570158,0.003135229,0.0002474897,0.0005108979,0.0003473196,0.0006538735,0.003719807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000407714,"about_ca_system_score_gemma":0.0006318565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01130241,"about_ca_topic_score_gemma":0.03734511,"domain_scores_codex":[0.9997397,0.00001182812,0.00002370987,0.00006648887,0.0001343626,0.00002389088],"domain_scores_gemma":[0.9992486,0.0001194528,0.0001049443,0.0001011407,0.0003821015,0.00004370391],"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":[0.001714575,0.0007368591,0.05983776,0.002220812,0.0003668983,0.001017616,0.000413235,0.006285091,0.4921862,0.001322808,0.2532893,0.1806088],"study_design_scores_gemma":[0.0002401789,0.0004748998,0.4268278,0.000186188,0.0002350768,0.001337513,0.0007848172,0.01129388,0.22735,0.001429866,0.3296139,0.0002259554],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.1167165,0.0003891504,0.02280916,0.0003021826,0.0003834963,0.0004118719,0.8341657,0.003079587,0.02174248],"genre_scores_gemma":[0.2255768,0.0006904675,0.08987162,0.0003516744,0.00008733651,0.001035696,0.673901,0.0007150963,0.007770293],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01524844,"threshold_uncertainty_score":0.05101115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08647340429276125,"score_gpt":0.3178683375115748,"score_spread":0.2313949332188136,"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."}}