{"id":"W4392857217","doi":"10.22541/essoar.171052494.46194670/v1","title":"Matrix corrected SIMS in-situ oxygen isotope analyses of marine shell aragonite for high resolution seawater temperature measurements","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Marine Bivalve and Aquaculture Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Seawater; Aragonite; In situ; Isotopes of oxygen; Matrix (chemical analysis); Oxygen; Analytical Chemistry (journal); Isotope; Calibration; Secondary ion mass spectrometry; Stable isotope ratio; High resolution; Mineralogy; Chemistry; Calcite; Geology; Oceanography; Mass spectrometry; Environmental chemistry; Nuclear chemistry; Remote sensing; Chromatography","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":[0.0002122874,0.0004878013,0.000298737,0.0006249561,0.0002543305,0.0005028577,0.0004026119,0.0002709156,0.00163756],"category_scores_gemma":[0.0005117374,0.0004231002,0.0003267739,0.0005500863,0.0001707987,0.000290095,0.0004158962,0.0003081817,0.0004314908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003056384,"about_ca_system_score_gemma":0.0003750993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004257024,"about_ca_topic_score_gemma":0.01264765,"domain_scores_codex":[0.9998016,0.00001602738,0.000009958071,0.00007743894,0.00007624658,0.00001865866],"domain_scores_gemma":[0.9997749,0.00001971521,0.0000412113,0.00005057507,0.00009895518,0.00001471858],"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":[0.0001122087,0.00001169349,0.005150877,0.00002636085,0.00002739312,0.00002913053,0.00004303694,0.001089882,0.9859489,0.0001385288,0.000124536,0.007297501],"study_design_scores_gemma":[0.00003413075,0.00009440631,0.07924977,0.00000855382,0.00008175993,0.0002363982,0.00007587489,0.07411657,0.8402457,0.0004438447,0.005380569,0.00003242804],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.93297,0.0001779281,0.06289146,0.00005264935,0.00005440522,0.00002338583,0.001136036,0.0008245362,0.001869635],"genre_scores_gemma":[0.9305502,0.0000921512,0.06528044,0.00002737443,0.00001421204,0.00003086225,0.001415367,0.0003865643,0.002202951],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004257024,"threshold_uncertainty_score":0.008464515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03778250367858634,"score_gpt":0.3077014001878256,"score_spread":0.2699188965092393,"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."}}