{"id":"W4411089522","doi":"10.1080/17538947.2025.2513044","title":"Automating nested watershed delineation over the world considering endorheic basins and islands","year":2025,"lang":"en","type":"article","venue":"International Journal of Digital Earth","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Gansu Province; National Natural Science Foundation of China","keywords":"Watershed; Geography; Cartography; Remote sensing; Computer science; Computer vision","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005901721,0.0003649561,0.0004023536,0.001918874,0.0004127418,0.00075424,0.0007212826,0.0003358114,0.0009059475],"category_scores_gemma":[0.0018911,0.0001837092,0.0005608382,0.001891947,0.0002714857,0.0009358823,0.001208504,0.0005065565,0.0003364396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005066647,"about_ca_system_score_gemma":0.00126523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0206473,"about_ca_topic_score_gemma":0.05660743,"domain_scores_codex":[0.9995757,0.00005860514,0.0000460583,0.0001732751,0.0001012819,0.00004508457],"domain_scores_gemma":[0.9994057,0.0001311433,0.00007618754,0.0001778263,0.0001709761,0.00003822345],"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":[0.0002034747,0.0003000276,0.1761634,0.0004664935,0.0002449577,0.0008666978,0.001456895,0.2190846,0.04442055,0.01368341,0.03518675,0.5079226],"study_design_scores_gemma":[0.0000684489,0.00004640167,0.1077003,0.000082898,0.00007398684,0.000264933,0.0009831355,0.7965142,0.02567496,0.01076578,0.05775726,0.00006774829],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5748054,0.0003092833,0.3503464,0.0003474415,0.00005710304,0.0003871313,0.06088433,0.007229331,0.005633651],"genre_scores_gemma":[0.5179567,0.0002022899,0.3828709,0.00005492392,0.00001360589,0.0002899748,0.0970892,0.0003357862,0.001186603],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0206473,"threshold_uncertainty_score":0.04105425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008174499624446267,"score_gpt":0.2431955403578911,"score_spread":0.2350210407334449,"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."}}