{"id":"W2887373131","doi":"10.1016/j.pacs.2018.07.005","title":"The application of frequency-domain photoacoustics to temperature-dependent measurements of the Grüneisen parameter in lipids","year":2018,"lang":"en","type":"article","venue":"Photoacoustics","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of British Columbia","funders":"Helmholtz Zentrum München; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; California HIV/AIDS Research Program; Canadian Institutes of Health Research; Alexander von Humboldt-Stiftung","keywords":"Photoacoustic imaging in biomedicine; Grüneisen parameter; Materials science; Characterization (materials science); Absorption (acoustics); Biomedical engineering; Analytical Chemistry (journal); Thermal expansion; Chemistry; Optics; Nanotechnology; Composite material; Chromatography; Physics; Medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0007453762,0.0002707614,0.0003228968,0.0001101016,0.000148408,0.00003468348,0.000713269,0.0001319971,0.00001335539],"category_scores_gemma":[0.0004796161,0.0001879761,0.00009536448,0.0006390953,0.0002703702,0.00005655832,0.00009345581,0.0003401245,0.000009212241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002129346,"about_ca_system_score_gemma":0.0001022349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001514667,"about_ca_topic_score_gemma":0.0002747575,"domain_scores_codex":[0.9978606,0.00007247546,0.0006822638,0.0002643089,0.0006304304,0.0004899863],"domain_scores_gemma":[0.9982848,0.0003384266,0.0001429174,0.0008444582,0.0002935023,0.00009586407],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002914093,0.00006884772,0.003101146,0.0001141497,0.00006139739,8.937211e-7,0.0009260548,0.01503916,0.9793205,0.000100779,0.0006831299,0.000554769],"study_design_scores_gemma":[0.001048337,0.0001291787,0.005816794,0.0002836736,0.0001798233,0.00001704624,0.001075765,0.1250173,0.8620762,0.003203726,0.0005784226,0.0005737359],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.888956,0.0002516191,0.1031339,0.0000842697,0.001882259,0.001625409,0.000203407,0.0001154858,0.003747718],"genre_scores_gemma":[0.9940849,0.00002816078,0.005376131,0.0001388371,0.0001622057,0.0001051787,0.000003705273,0.00005755379,0.00004332851],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1172443,"threshold_uncertainty_score":0.7665444,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01164656662732754,"score_gpt":0.2315463787710069,"score_spread":0.2198998121436793,"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."}}