{"id":"W4388729121","doi":"10.1038/s41597-023-02732-9","title":"Canada Source Watershed Polygons (Can-SWaP): A dataset for the protection of Canada’s municipal water supply","year":2023,"lang":"en","type":"article","venue":"Scientific Data","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Pacific Salmon Foundation; Ontario Forest Research Institute; Canadian Forest Service","funders":"Natural Resources Canada; U.S. Forest Service; Indigenous Services Canada","keywords":"Swap (finance); Watershed; Environmental science; Water supply; Surface water; Water resource management; STREAMS; Hydrology (agriculture); Environmental resource management; Business; Environmental engineering; Computer science; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00119275,0.0001155548,0.0001181858,0.00003650095,0.001038706,0.00004640561,0.001452734,0.00002375754,0.000368403],"category_scores_gemma":[0.00007124284,0.00006761491,0.00001631558,0.0002476857,0.0004310892,0.0001553935,0.00219144,0.00007783863,0.00004481154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001083272,"about_ca_system_score_gemma":0.000145809,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9896568,"about_ca_topic_score_gemma":0.9991325,"domain_scores_codex":[0.9982961,0.00004793028,0.0001950077,0.0005288954,0.0003980733,0.0005339898],"domain_scores_gemma":[0.9983773,0.00005688628,0.00004838781,0.00145062,0.000007948761,0.00005880812],"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":[0.00001816122,0.00001012923,0.0004656944,0.0000150176,0.0000402158,0.000003477575,0.0003557601,0.001393408,0.001458469,0.000005570523,0.9956487,0.0005853893],"study_design_scores_gemma":[0.0001787945,0.00001287962,0.002090688,0.000003591332,0.00003866983,0.000001124687,0.0005848692,0.01148942,0.004067455,0.0000804475,0.9813429,0.0001092317],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5864483,0.00005541863,0.001144112,0.1647041,0.006898304,0.005039454,0.2345239,0.0001473129,0.001039057],"genre_scores_gemma":[0.9582328,0.000003276433,0.00004377261,0.0005172043,0.0000280875,0.00008225849,0.02568157,0.00001134999,0.01539967],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3717845,"threshold_uncertainty_score":0.7988992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03513598247150342,"score_gpt":0.232121808342357,"score_spread":0.1969858258708536,"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."}}