{"id":"W3098481921","doi":"10.3390/land9110444","title":"A Transparent and Intuitive Modeling Framework and Software for Efficient Land Allocation","year":2020,"lang":"en","type":"article","venue":"Land","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"BAH Enterprises (Canada)","funders":"National Research Foundation","keywords":"Computer science; Software; Set (abstract data type); Adjacency list; Land-use planning; Land use; Budget constraint; Operations research; Environmental resource management; Environmental science; Engineering; Civil engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000457411,0.0000468641,0.00006571603,0.000004662508,0.0001067137,0.000008105183,0.00002125773,0.00003394593,0.00001391554],"category_scores_gemma":[0.00006536696,0.00003973143,0.000007445508,0.0000331666,0.00004626004,0.00002313501,0.00002624068,0.00004188435,0.00000351943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008400187,"about_ca_system_score_gemma":0.000001735594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003651761,"about_ca_topic_score_gemma":0.0004174951,"domain_scores_codex":[0.999685,0.000008560647,0.00006010313,0.0001372847,0.00003658462,0.00007247368],"domain_scores_gemma":[0.9998389,0.00007903061,0.00001489391,0.0000241172,0.000005418252,0.00003759073],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004135069,0.0000154725,0.8718679,0.00003010162,0.00001405544,5.405109e-7,0.005190064,0.1206114,0.00002836465,0.0002419492,0.0000162628,0.00194256],"study_design_scores_gemma":[0.0003635193,0.00008532605,0.1298098,0.00001250616,0.00002066663,9.104738e-7,0.0001217531,0.8662376,0.000008081966,0.003190508,0.00006688412,0.00008243773],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6920128,0.0001214902,0.3067212,0.0009465031,0.00001650354,0.0001191041,0.000007354513,0.00001214263,0.00004295494],"genre_scores_gemma":[0.9948778,0.00005872755,0.004630624,0.0003790949,0.00001398064,0.00002188258,0.00000564887,0.000003371487,0.000008875199],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7456262,"threshold_uncertainty_score":0.1620201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0221800535946911,"score_gpt":0.2483377298139134,"score_spread":0.2261576762192223,"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."}}