{"id":"W4394227096","doi":"10.6084/m9.figshare.3516536","title":"Appendix D. Values of the validity functions (i.e., fuzziness performance index and normalized classification entropy) for and appropriate number of subpopulations indicated by fuzzy clustering of movement data for migratory and tundra-wintering barren-ground, Dolphin and Union island, and boreal caribou in the Northwest Territories, Nunavut, and northern Alberta, Canada.","year":2016,"lang":"en","type":"dataset","venue":"Figshare","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cluster analysis; Data mining; Fuzzy logic; Entropy (arrow of time); Mathematics; Index (typography); Computer science; Artificial intelligence; Pattern recognition (psychology); Physics","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":[0.001303848,0.001515485,0.0009184211,0.002432366,0.0008424689,0.001997525,0.002326403,0.001528708,0.09357323],"category_scores_gemma":[0.00930017,0.0006978335,0.001071533,0.004005738,0.0004657478,0.0008202642,0.001230631,0.001354481,0.05651273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003140015,"about_ca_system_score_gemma":0.004275238,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2071336,"about_ca_topic_score_gemma":0.4100648,"domain_scores_codex":[0.9992383,0.00009919158,0.000106531,0.0002027215,0.0002371942,0.0001161523],"domain_scores_gemma":[0.9943674,0.001729731,0.0003466029,0.000890171,0.002380092,0.0002859699],"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":[0.00002766081,0.00002203546,0.003742039,0.0004073782,0.00002850986,0.00001639848,0.0000210604,0.0007416573,0.00004927419,0.0003014444,0.9927763,0.001866274],"study_design_scores_gemma":[0.0006665278,0.00002216034,0.0374601,0.0007078091,0.00005921785,0.00009118535,0.0002607895,0.002409553,0.000389915,0.002419329,0.9554541,0.00005931699],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009810642,0.00001570511,0.00004331531,0.00002585659,0.00000886215,0.000008545533,0.9994578,0.00008741416,0.0002543874],"genre_scores_gemma":[0.0007970395,0.00002184483,0.0004083244,0.0000246646,0.000004298623,0.00008147187,0.9980261,0.0000310998,0.000605211],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7928664,"threshold_uncertainty_score":0.4118559,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04330029465862422,"score_gpt":0.2511020290277109,"score_spread":0.2078017343690867,"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."}}