{"id":"W2786131173","doi":"10.4236/nr.2018.91001","title":"Loon Nest Viability Model: A Performance Indicator for Improving Water-Level Regulation of Large Water Bodies","year":2018,"lang":"en","type":"article","venue":"Natural Resources","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Center for Northern Studies; Environment and Climate Change Canada","funders":"Environment and Climate Change Canada","keywords":"Wetland; Nest (protein structural motif); Environmental science; Ecosystem; Water level; Ecology; Hydrology (agriculture); Range (aeronautics); Geography; Geology; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009564465,0.0005686399,0.0004356571,0.0003741384,0.0002267578,0.0005640865,0.001396712,0.0007079678,0.001545655],"category_scores_gemma":[0.002273929,0.0002568061,0.0004008051,0.0002332526,0.0003805038,0.0004776849,0.0005458941,0.000496386,0.0001232617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009973543,"about_ca_system_score_gemma":0.0008743011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03309428,"about_ca_topic_score_gemma":0.0224184,"domain_scores_codex":[0.9997985,0.00006085135,0.000009539937,0.00006570182,0.00002374129,0.00004173869],"domain_scores_gemma":[0.999112,0.0004375772,0.0002248576,0.00004059476,0.0001161579,0.00006885399],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005629801,0.00003131028,0.01037789,0.000008955082,0.00002049395,0.00003121904,0.00001561791,0.9860401,0.0004060681,0.0007438901,0.0001756067,0.002092503],"study_design_scores_gemma":[0.000005864033,0.00001382195,0.00101335,0.000001343914,0.000004776272,0.000003442097,0.000003393017,0.998662,0.00008684886,0.0001527424,0.00005050427,0.000001912798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8927025,0.0001218559,0.1010778,0.0002440511,0.00002184225,0.0001110398,0.0008997781,0.0004950229,0.004326184],"genre_scores_gemma":[0.9939491,0.00003065906,0.004551826,0.00001958217,0.000006104058,0.00006833259,0.0003100134,0.00001655311,0.001047935],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03309428,"threshold_uncertainty_score":0.06580329,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008739756342824008,"score_gpt":0.2232906335006615,"score_spread":0.2145508771578375,"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."}}