{"id":"W4238195995","doi":"10.7287/peerj.preprints.15","title":"GenGIS 2: Geospatial analysis of traditional and genetic biodiversity, with new gradient algorithms and an extensible plugin framework","year":2013,"lang":"en","type":"preprint","venue":"","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick; Dalhousie University","funders":"","keywords":"Plug-in; Geospatial analysis; Computer science; Data mining; Set (abstract data type); Visualization; Geographic information system; Range (aeronautics); Source code; Geography; Cartography; Programming language","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.003155632,0.00163897,0.001452399,0.003124344,0.0005380613,0.002592383,0.002884804,0.0007486098,0.03638808],"category_scores_gemma":[0.008601874,0.001892392,0.0020897,0.003166893,0.0007270995,0.003296365,0.003520798,0.002413949,0.01484024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006916697,"about_ca_system_score_gemma":0.0021428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008067992,"about_ca_topic_score_gemma":0.008767083,"domain_scores_codex":[0.9986408,0.0002320649,0.0002142129,0.0003287734,0.000490307,0.00009377138],"domain_scores_gemma":[0.9980096,0.0009587673,0.0001675875,0.0003421227,0.0004164576,0.0001055022],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005849604,0.0001592346,0.00832834,0.001448397,0.0007096697,0.0005021353,0.00160562,0.01848658,0.007635062,0.02802061,0.6241281,0.3083913],"study_design_scores_gemma":[0.0003968511,0.0001007483,0.01056167,0.0003331165,0.0002009341,0.0008265824,0.0003976842,0.1590227,0.01592557,0.05977588,0.7520054,0.0004528957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004631577,0.0002681429,0.6097606,0.0005002,0.0003343917,0.000462016,0.03663577,0.3404488,0.006958502],"genre_scores_gemma":[0.02554294,0.0004326806,0.824473,0.0005464277,0.0001303235,0.002276444,0.05108858,0.08985308,0.005656525],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03638808,"threshold_uncertainty_score":0.1217302,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04234343658816507,"score_gpt":0.239926685058244,"score_spread":0.1975832484700789,"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."}}