{"id":"W7073761713","doi":"","title":"Geographical gradients in diet affect population dynamics of Canada lynx","year":2007,"lang":"en","type":"article","venue":"Journal of International Crisis and Risk Communication Research","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"California Department of Fish and Wildlife; Washington State University; Idaho Department of Fish and Game; University of Minnesota; Massachusetts Department of Fish and Game; Smithsonian Institution","keywords":"Predation; Snowshoe hare; Population cycle; Generalist and specialist species; Predator; Herbivore; Population; Taiga","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00437456,0.00004703724,0.0001135795,0.0005155939,0.0001119352,0.00002630772,0.0003864698,0.00004004258,0.00007409662],"category_scores_gemma":[0.0002986088,0.00003678095,0.00003696988,0.0002650371,0.00007763926,0.0001574815,0.00003340903,0.0004461009,3.329345e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005018735,"about_ca_system_score_gemma":0.00006720293,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6487412,"about_ca_topic_score_gemma":0.4016597,"domain_scores_codex":[0.9985075,0.0002563489,0.0003956928,0.0000672665,0.000641175,0.0001319961],"domain_scores_gemma":[0.9986547,0.0004936462,0.0002391719,0.0001328732,0.0004099571,0.00006967384],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000145848,0.00002521462,0.9783607,0.000004220712,0.00001730759,0.000002763276,0.00007863284,0.0001032121,0.000001731473,0.0002699521,0.002470591,0.01851981],"study_design_scores_gemma":[0.0001984724,0.0001244483,0.9772276,0.00004988215,0.00000347404,0.00001151903,0.0009579582,0.01650796,0.00006745869,0.003195776,0.001616661,0.00003876989],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956249,0.0007784451,0.0001732682,0.002092132,0.0001053681,0.00004901728,0.00002164033,0.000002414926,0.001152828],"genre_scores_gemma":[0.9933513,0.005358054,0.001153962,0.00006130012,0.00002095068,1.473248e-7,0.00003421255,0.000001587471,0.00001851947],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2470815,"threshold_uncertainty_score":0.6092584,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02167319456734791,"score_gpt":0.3242905743292736,"score_spread":0.3026173797619257,"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."}}