{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003636509,0.0001436912,0.0002084781,0.000115151,0.0002032272,0.0001682307,0.003077291,0.00004711323,0.008065721],"category_scores_gemma":[0.0002548801,0.00009246325,0.00006843404,0.0004833575,0.00001057499,0.0001844323,0.001076244,0.0002441914,0.00001200519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002810415,"about_ca_system_score_gemma":0.00006591297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001527401,"about_ca_topic_score_gemma":0.0005378986,"domain_scores_codex":[0.9988009,0.00004018309,0.0002986186,0.0002651197,0.0003786853,0.000216458],"domain_scores_gemma":[0.9979297,0.000815219,0.0003641141,0.0008313083,0.00004590645,0.00001374913],"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":[0.000004437945,0.00007625047,0.000006556073,0.0002969639,0.00001756976,0.000002777202,0.0001222465,0.0001674696,7.479427e-8,0.001685791,0.9965163,0.001103541],"study_design_scores_gemma":[0.000261172,0.00005895469,0.0008563441,0.0001599548,0.00002453727,0.000001502932,0.0002733224,0.0003824375,0.000003805424,0.0008601172,0.996992,0.0001258614],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[5.561055e-7,0.00007912447,0.00009412543,0.0001577007,0.0001074085,0.001319961,0.997668,0.00001186583,0.0005612361],"genre_scores_gemma":[0.000133177,0.000008951013,0.0002831193,0.0001757433,0.00006764921,0.001612571,0.9976884,0.000005534977,0.00002492851],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.008053715,"threshold_uncertainty_score":0.9928411,"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."}}