{"id":"W6931940681","doi":"10.5683/sp3/zyiq2u","title":"Mapping the Unseen: Identifying Data Gaps and Proposing New Sampling Points in Northern Boreal Mountain Eco-province, BC Using K-Means Clustering and cLHS","year":2024,"lang":"en","type":"dataset","venue":"Borealis","topic":"Marine and environmental studies","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Representativeness heuristic; Sampling (signal processing); Wetland; Boreal; Cluster analysis; Soil water; Resource (disambiguation); Principal component analysis; Soil map","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006191375,0.0003706294,0.0003747058,0.0001724931,0.0004187305,0.0005028813,0.0005145757,0.000137621,0.00004618207],"category_scores_gemma":[0.00004461777,0.0002749903,0.00003418748,0.0001804537,0.0001696288,0.0003591847,0.0007872711,0.00046519,0.00001429117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000377369,"about_ca_system_score_gemma":0.00008420373,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6618955,"about_ca_topic_score_gemma":0.9085277,"domain_scores_codex":[0.9979712,0.00008435987,0.0004219746,0.000755446,0.0003110713,0.0004559178],"domain_scores_gemma":[0.9989967,0.0001371676,0.0001460926,0.0005915851,0.000006112462,0.000122293],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004734413,0.0000146912,0.2831797,0.001144983,0.0001683759,0.0002357624,0.0008565611,0.001439704,0.00000318709,0.000001170199,0.6384478,0.07446071],"study_design_scores_gemma":[0.0002688311,0.00003514456,0.1397022,0.0008054173,0.0001448216,0.0001240365,0.001607713,0.0318777,6.690041e-7,0.0002362741,0.8246338,0.000563391],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.008479374,0.005314886,0.0001734155,0.0002968152,0.0001868883,0.0004348874,0.984746,0.00002837635,0.0003393737],"genre_scores_gemma":[0.0009441107,0.004529376,0.001286948,0.0001982615,0.0004279372,0.000001627464,0.9925612,0.00001839913,0.00003220378],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2466321,"threshold_uncertainty_score":0.9999703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06552851587348593,"score_gpt":0.2658110592135705,"score_spread":0.2002825433400845,"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."}}