{"id":"W4407468453","doi":"10.2139/ssrn.5137157","title":"Dealing with the Heat: Assessing Heat Stress in an Arctic Seabird Using 3d-Printed Thermal Models","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Physiological and biochemical adaptations","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor; Brock University; Université du Québec à Rimouski; McMaster University","funders":"","keywords":"Seabird; Heat stress; Arctic; The arctic; 3d printed; Environmental science; Computer science; Oceanography; Engineering; Geology; Ecology; Atmospheric sciences; Biology; Manufacturing 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":[0.0001810105,0.0004177976,0.0002963309,0.0003768823,0.0002418362,0.0009272259,0.0003782178,0.0009094918,0.001432286],"category_scores_gemma":[0.0004790581,0.0002558961,0.0004832522,0.0003143067,0.0003655686,0.0003385824,0.0005047158,0.0002305927,0.0003532439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001947019,"about_ca_system_score_gemma":0.0003491692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005148096,"about_ca_topic_score_gemma":0.009115403,"domain_scores_codex":[0.9998604,0.00002290549,0.000004941684,0.00004123841,0.00005324442,0.00001725515],"domain_scores_gemma":[0.9998547,0.00005682641,0.00001830621,0.00002126613,0.00002840314,0.00002052138],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007804815,0.0004045362,0.03663899,0.000449894,0.0002161674,0.0006506448,0.001247254,0.4414371,0.3823294,0.0007223149,0.001059607,0.1340636],"study_design_scores_gemma":[0.00002611562,0.0007213943,0.0757076,0.00006879631,0.0002447756,0.0007189959,0.0007651796,0.8418128,0.07549544,0.001426508,0.002884245,0.0001281059],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9046544,0.0002321062,0.09074809,0.00009834127,0.00006901044,0.00006696009,0.0005358999,0.0004768546,0.003118329],"genre_scores_gemma":[0.9568093,0.000239673,0.04100754,0.00003868344,0.00001105521,0.00004428299,0.000258884,0.00005165734,0.001538945],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005148096,"threshold_uncertainty_score":0.01023626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02785301747049525,"score_gpt":0.2755741260327146,"score_spread":0.2477211085622194,"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."}}