{"id":"W2754112907","doi":"10.1039/c7ja00191f","title":"“Non-invasive” portable laser ablation sampling of art and archaeological materials with subsequent Sr–Nd isotope analysis by TIMS using 10<sup>13</sup> Ω amplifiers","year":2017,"lang":"en","type":"article","venue":"Journal of Analytical Atomic Spectrometry","topic":"Cultural Heritage Materials Analysis","field":"Arts and Humanities","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Innovation Cluster (Canada)","funders":"Horizon 2020 Framework Programme; FP7 Ideas: European Research Council; Santen; European Commission","keywords":"Laser ablation; Isotope; Sampling (signal processing); Laser; Isotope analysis; Amplifier; Ablation; Archaeology; Analytical Chemistry (journal); Radiochemistry; Materials science; Chemistry; Geology; Optics; Environmental chemistry; Optoelectronics; Physics; History; Engineering; Nuclear physics","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":[0.000506705,0.0006757369,0.000495402,0.001442284,0.0006019713,0.0008119491,0.00202072,0.001063701,0.005660646],"category_scores_gemma":[0.0007845807,0.0006675584,0.000547384,0.0009607404,0.0009316648,0.001303476,0.001359788,0.001103991,0.002467732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004798534,"about_ca_system_score_gemma":0.0006234471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007327083,"about_ca_topic_score_gemma":0.002445569,"domain_scores_codex":[0.9992175,0.00005635481,0.00003246707,0.0002388746,0.0004074836,0.00004720824],"domain_scores_gemma":[0.9993371,0.0001515037,0.0001191307,0.0002043631,0.0001545404,0.00003337885],"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":[0.0001075732,0.00002838107,0.001738067,0.0001353549,0.00002440709,0.00006842371,0.00007939342,0.000221505,0.9499912,0.0006310354,0.0003477435,0.04662693],"study_design_scores_gemma":[0.00002103563,0.0003172659,0.00908124,0.00002466506,0.00005970291,0.001424807,0.0001435647,0.008098391,0.9638022,0.001100864,0.01587301,0.00005332853],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1353899,0.0009897093,0.8501209,0.0002325982,0.0001572313,0.0003546718,0.001058064,0.003194323,0.008502603],"genre_scores_gemma":[0.2210193,0.0007128378,0.7671199,0.0001533494,0.00008634399,0.000380585,0.0006268817,0.0003591201,0.009541567],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005660646,"threshold_uncertainty_score":0.01893675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0402950375433991,"score_gpt":0.2693475008442951,"score_spread":0.229052463300896,"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."}}