{"id":"W2530955115","doi":"10.1038/sdata.2016.87","title":"Long-term, high frequency in situ measurements of intertidal mussel bed temperatures using biomimetic sensors","year":2016,"lang":"en","type":"article","venue":"Scientific Data","topic":"Physiological and biochemical adaptations","field":"Environmental Science","cited_by":98,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Environment Research Council; National Research Foundation; National Oceanic and Atmospheric Administration; Sight Research UK; National Aeronautics and Space Administration; David and Lucile Packard Foundation; National Geographic Society; National Science Foundation","keywords":"Intertidal zone; Ectotherm; Mussel; Environmental science; Habitat; Data logger; Intertidal ecology; Oceanography; Atmospheric sciences; Ecology; Biology; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003763311,0.0001037902,0.0001278917,0.00005196537,0.00007819535,0.00003855209,0.0008758267,0.00004191025,0.0006782966],"category_scores_gemma":[0.0002380413,0.00005948619,0.00002808181,0.0004315227,0.0006291932,0.000329121,0.0007244388,0.00005301532,0.0001523143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006817764,"about_ca_system_score_gemma":0.00001456052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001629781,"about_ca_topic_score_gemma":0.0002464123,"domain_scores_codex":[0.9985133,0.00006136142,0.000255138,0.0005865017,0.000345178,0.0002384716],"domain_scores_gemma":[0.9990575,0.00004777365,0.00007499629,0.0007273352,0.00001520994,0.00007721893],"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":[0.000007929623,0.000127181,0.02183353,0.000004963123,0.000004628892,0.000002505529,0.00002465265,0.000004517002,0.9762167,0.000003390604,0.0005810436,0.001188954],"study_design_scores_gemma":[0.000325649,0.00001892984,0.1886961,0.0001019884,0.00001237583,0.000001941551,0.00001080578,0.00009912239,0.8096905,0.0008544236,0.00002898135,0.0001592568],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988576,0.0000188899,0.00007390371,0.0001654909,0.0002420233,0.0001155544,0.0003899866,0.00001313556,0.0001234651],"genre_scores_gemma":[0.9976898,0.000001264852,0.001881502,0.00002701575,0.00001359156,0.00000184113,0.0002042019,0.000004135676,0.0001766721],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1668625,"threshold_uncertainty_score":0.7426871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08470878739218475,"score_gpt":0.2839162326669961,"score_spread":0.1992074452748113,"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."}}