{"id":"W2054313318","doi":"10.1007/s10661-014-3732-7","title":"A comparison of two procedures to estimate three basic monitoring landscape metrics for monitoring","year":2014,"lang":"en","type":"article","venue":"Environmental Monitoring and Assessment","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Alberta Biodiversity Monitoring Institute","keywords":"Estimator; Statistics; Metric (unit); Sample (material); Sampling (signal processing); Sample size determination; Mathematics; Land cover; Variance (accounting); Mean squared error; Estimation; Computer science; Land use; Ecology; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.01104077,0.001777053,0.001067905,0.004940945,0.0007406421,0.00156862,0.00247424,0.002028613,0.002771216],"category_scores_gemma":[0.04793925,0.0006040608,0.001137901,0.003026038,0.0007557861,0.002401251,0.001831288,0.001146483,0.0008460307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001590108,"about_ca_system_score_gemma":0.001480928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01389433,"about_ca_topic_score_gemma":0.01917273,"domain_scores_codex":[0.9923406,0.003191636,0.0004529964,0.0009166609,0.00285893,0.000239197],"domain_scores_gemma":[0.9509328,0.03809595,0.001706109,0.002512591,0.006383325,0.00036929],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005122222,0.001612224,0.07933719,0.001110218,0.001325182,0.0001410227,0.00108021,0.03724504,0.02120454,0.00844469,0.003865089,0.8395123],"study_design_scores_gemma":[0.001535186,0.005146491,0.3234704,0.0003185461,0.001834861,0.001812873,0.001218801,0.6092875,0.03577253,0.01020126,0.008707806,0.0006937842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.165109,0.000996241,0.8250867,0.0003305452,0.0002517047,0.001444077,0.0009055899,0.001616442,0.004259755],"genre_scores_gemma":[0.2924585,0.0005151738,0.7024977,0.0001722521,0.00005890127,0.00152788,0.0008135382,0.0003839168,0.001572129],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01389433,"threshold_uncertainty_score":0.0583899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02369278050422039,"score_gpt":0.3344919849940323,"score_spread":0.3107992044898119,"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."}}