{"id":"W4255484906","doi":"10.5194/hessd-3-3439-2006","title":"Uncertainties in land use data","year":2006,"lang":"en","type":"preprint","venue":"","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Categorical variable; Metadata; Context (archaeology); Confusion; Computer science; Variable (mathematics); Land use; Data mining; Confusion matrix; Econometrics; Geography; Mathematics; Artificial intelligence; Machine learning; Civil engineering; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.006144594,0.0003948712,0.0005688258,0.002800361,0.0004637917,0.004137633,0.0008338657,0.0006514007,0.001385434],"category_scores_gemma":[0.03591209,0.0002839583,0.000519574,0.004449167,0.001311765,0.003280085,0.001619733,0.000976897,0.0004680188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001398791,"about_ca_system_score_gemma":0.0007456018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003806112,"about_ca_topic_score_gemma":0.003067376,"domain_scores_codex":[0.9932956,0.002542682,0.000622234,0.0007817807,0.002646229,0.0001113971],"domain_scores_gemma":[0.9769239,0.0148926,0.001895878,0.003756712,0.002406843,0.0001241724],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002053526,0.00006824244,0.1020596,0.0009936212,0.0005848389,0.0007077741,0.001539858,0.2297083,0.003532776,0.2576202,0.01184227,0.3911371],"study_design_scores_gemma":[0.00001964249,0.00008793975,0.0505483,0.0009032498,0.0001314071,0.001052209,0.001696633,0.2485564,0.007911454,0.580997,0.1079112,0.0001846061],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1615462,0.006160318,0.7746674,0.005580504,0.0007177636,0.000196326,0.02480584,0.000775834,0.02554969],"genre_scores_gemma":[0.9111012,0.00228559,0.07320065,0.0007111891,0.0002554213,0.0001351968,0.009977044,0.0001013725,0.002232266],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006144594,"threshold_uncertainty_score":0.03249615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0463831736818329,"score_gpt":0.258152392417671,"score_spread":0.2117692187358381,"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."}}