{"id":"W4393776218","doi":"10.5281/zenodo.3690046","title":"National Forestry Database - Base de données nationales des forêts - Canada","year":2019,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Forest Ecology and Biodiversity Studies","field":"Agricultural and Biological Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Forestry; Database; Geography; Computer science","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":[0.007007385,0.00179187,0.00251524,0.02093463,0.00577208,0.01320976,0.005248049,0.002016056,0.08610192],"category_scores_gemma":[0.04171279,0.001175155,0.001795745,0.03913105,0.001262101,0.005955076,0.004451783,0.003087956,0.06812259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04169013,"about_ca_system_score_gemma":0.166657,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9299735,"about_ca_topic_score_gemma":0.9158086,"domain_scores_codex":[0.9856185,0.0009357771,0.002762389,0.001731208,0.007877296,0.001074702],"domain_scores_gemma":[0.9299884,0.005532349,0.002069239,0.004846854,0.05496219,0.002600868],"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":[0.0001051023,0.00003498807,0.002654625,0.001338721,0.00009316495,0.00007908564,0.0002445823,0.0005463319,0.0002682041,0.01051876,0.9422569,0.04185956],"study_design_scores_gemma":[0.00003627329,0.000004379057,0.00470815,0.000882707,0.00003685196,0.00005656163,0.0002454572,0.0006899902,0.000377277,0.002378197,0.9904947,0.00008931952],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0006667803,0.001662059,0.004552417,0.001195992,0.0001970952,0.000507782,0.9492858,0.002713835,0.03921809],"genre_scores_gemma":[0.003507587,0.002555977,0.01128517,0.0005167291,0.00003596196,0.0005570818,0.9675196,0.0007527568,0.013269],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08610192,"threshold_uncertainty_score":0.3024845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06219431134463054,"score_gpt":0.224021493884314,"score_spread":0.1618271825396834,"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."}}