{"id":"W4394078363","doi":"10.6084/m9.figshare.19929380.v2","title":"FUZZY LOGIC APPLIED IN THE PROSPECTING OF SUITABLE AREAS FOR THE ESTABLISHMENT OF COMMERCIAL FOREST PLANTATIONS","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Prospecting; Fuzzy logic; Computer science; Agroforestry; Forestry; Agricultural engineering; Artificial intelligence; Environmental science; Geography; Mining engineering; Engineering","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":[0.001425924,0.001318247,0.0009423125,0.003616163,0.0007384658,0.001929183,0.002722243,0.001785828,0.04487637],"category_scores_gemma":[0.008376923,0.0004333271,0.001409664,0.005211357,0.0003360435,0.001064396,0.001401134,0.001493216,0.03600528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002006482,"about_ca_system_score_gemma":0.00199617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03738828,"about_ca_topic_score_gemma":0.09454395,"domain_scores_codex":[0.9991344,0.0001822022,0.0001187662,0.0002386393,0.0002113187,0.0001147677],"domain_scores_gemma":[0.9970554,0.001352727,0.0002348607,0.0005317432,0.0006739099,0.0001513237],"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":[0.0001274771,0.00007103501,0.004170459,0.001138179,0.00005769479,0.00003968347,0.00004745003,0.002907147,0.0001090671,0.001584711,0.9811999,0.008547052],"study_design_scores_gemma":[0.0005164625,0.00005795085,0.0262649,0.001067949,0.0001203984,0.0001368868,0.0003805391,0.01031457,0.0008128166,0.01026651,0.9499623,0.00009881736],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005996043,0.00007719062,0.0003197296,0.0001342941,0.00004735997,0.00002623542,0.9973295,0.0002705188,0.001195601],"genre_scores_gemma":[0.002462778,0.0000987191,0.002049364,0.00006368595,0.00001166882,0.0001700806,0.9941505,0.00004857871,0.0009447737],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04487637,"threshold_uncertainty_score":0.1501264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05806079859651685,"score_gpt":0.284850170661525,"score_spread":0.2267893720650082,"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."}}