{"id":"W2529486232","doi":"10.1553/eco.mont-8-1s44","title":"Indicators of climate: Ecrins National Park participates in long-term monitoring to help determine the effects of climate change","year":2015,"lang":"en","type":"article","venue":"eco mont (Journal on Protected Mountain Areas Research)","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"BIO (Canada)","funders":"","keywords":"National park; Climate change; Glacier; Environmental resource management; General partnership; Vegetation (pathology); Work (physics); Geography; Environmental science; Term (time); Ecosystem; Environmental planning; Physical geography; Ecology; Political science; Engineering","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":[0.002175901,0.000611003,0.000309783,0.002405479,0.0004953112,0.001065452,0.0008750211,0.0002697475,0.01234074],"category_scores_gemma":[0.001904092,0.000162898,0.0001340107,0.002564448,0.0002470771,0.0008115565,0.001350251,0.0007296056,0.002477156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007272148,"about_ca_system_score_gemma":0.002023944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02321003,"about_ca_topic_score_gemma":0.05650056,"domain_scores_codex":[0.9985625,0.0003023942,0.00009313253,0.0002893914,0.0006093477,0.0001432982],"domain_scores_gemma":[0.9947753,0.0004037584,0.0008519882,0.0003786526,0.002635216,0.0009549486],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000403381,0.0003947564,0.4183198,0.0006844138,0.0001497854,0.0003670996,0.001027628,0.001241284,0.005245319,0.002858275,0.2905572,0.278751],"study_design_scores_gemma":[0.00003430454,0.0001225009,0.5514665,0.0001022511,0.00003147818,0.0003220778,0.0006471582,0.002233832,0.002678248,0.0004155644,0.4419095,0.0000365939],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3452028,0.003873195,0.02391967,0.004704133,0.001481444,0.002115112,0.3046912,0.003998343,0.3100141],"genre_scores_gemma":[0.6431012,0.001456195,0.07317279,0.0005311725,0.0003460601,0.001391276,0.1656474,0.001065907,0.1132879],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02321003,"threshold_uncertainty_score":0.04614985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0824107494509493,"score_gpt":0.3574505378896511,"score_spread":0.2750397884387019,"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."}}