{"id":"W1982215158","doi":"10.1002/2013wr013935","title":"LiDAR‐derived snowpack data sets from mixed conifer forests across the Western United States","year":2014,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":116,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"U.S. Department of Agriculture; National Science Foundation","keywords":"Snowpack; Lidar; Snow; Watershed; Environmental science; Vegetation (pathology); Drainage basin; Structural basin; Remote sensing; Hydrology (agriculture); Physical geography; Geology; Geography; Geomorphology; Cartography","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001736323,0.0001747351,0.0002077593,0.0000441074,0.001279094,0.0005923621,0.001571461,0.00007690448,0.001061873],"category_scores_gemma":[0.0001950876,0.00008924657,0.00003954076,0.0004068022,0.0005637284,0.0001973526,0.0005986644,0.0004065712,0.0009743507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006395974,"about_ca_system_score_gemma":0.00001362079,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.05310982,"about_ca_topic_score_gemma":0.09545161,"domain_scores_codex":[0.9969046,0.0005928792,0.0002678431,0.0005149967,0.0007939584,0.0009257579],"domain_scores_gemma":[0.9972425,0.001219769,0.00003989535,0.001152712,0.0001750293,0.0001701516],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001135206,0.00002242148,0.9563417,0.00001598401,0.00008945502,0.00001237298,0.01467461,0.001558902,0.00005012674,0.000002011481,0.01403065,0.01308818],"study_design_scores_gemma":[0.000224043,0.0000586282,0.5257155,0.00001105719,0.000004871658,0.000001028058,0.001979338,0.01631695,0.00009915741,0.0002084164,0.4552858,0.00009521643],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9926389,0.0004016506,0.00005204691,0.005145254,0.0001565426,0.0002794347,0.0008910756,0.00004913031,0.0003860039],"genre_scores_gemma":[0.9927117,0.0001437895,0.0000952375,0.0005320549,0.0002721761,0.000006107871,0.004616086,0.00001143609,0.001611443],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4412551,"threshold_uncertainty_score":0.9998513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1145965243085511,"score_gpt":0.337351431035858,"score_spread":0.2227549067273069,"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."}}