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Enregistrement W4394049252 · doi:10.5281/zenodo.7006610

Northeast Pacific deoxygenation and volcanism during the last deglaciation

2022· dataset· en· W4394049252 sur OpenAlexaboutno aff
Jianghui Du, Alan C Mix, Brian A. Haley, Christina L. Belanger, Sharon Sharon

Notice bibliographique

RevueZenodo (CERN European Organization for Nuclear Research) · 2022
Typedataset
Langueen
DomaineEnvironmental Science
ThématiqueMethane Hydrates and Related Phenomena
Établissements canadiensnon disponible
Organismes subventionnairesHorizon 2020 Framework ProgrammeNational Science Foundation
Mots-clésDeglaciationVolcanismDeoxygenationGeologyOceanographyPaleontologyChemistry

Résumé

récupéré en direct d'OpenAlex

File structure: Source data Contains source data to main text and extended data figures. Data sources are identified within and listed below. Computer codes Geochemical inversion – “Data for geochemical inversion.xlsx”: contains Gulf of Alaska sediment and volcanic/terrigenous endmember geochemical data used for data inversion. “geochemical inversion.r”: R script used to perform geochemical inversion. “GOA inversion fraction.csv”: Result of the geochemical inversion, including the volcanic and terrigenous fractions in each Gulf of Alaska sediment sample. “GOA inversion residual.csv”: Result of the geochemical inversion, including the residuals of each element. cluster volcanic geochemical data – “Database of volcanic geochemistry.xlsx”: compiled database of the geochemistry of volcanic endmember samples. “cluster.r”: R script used to perform cluster analysis on the volcanic samples. “Clustered volcanic data.csv”: the results of cluster analysis. “Dendroplot.r”: R script used to plot the dendrogram for the cluster analysis. “dendro 15 complete euclidean.pdf”: The dendrogram. “Volcanic endmembers.csv”: final geochemical volcanic endmembers based on the cluster analysis. Global volcanic eruption compilation – “eruption.database.intcal20.xlsx”: This file includes the eruption database compiled by this study, as well as the previous compilation of Huybers and Langmuir 2009 EPSL (referred to as HL09). “eruption.ratio.R” and “volc.freq.R”: These are R scripts that compute the eruption frequency of glaciated and unglaciated volcanoes using the eruption database. “eruption.ratio.R”calls the function inside “volc.freq.R”. “Volcanic eruption summary.xlsx”: This file contains the outputs of the R scripts. PISM sensitivity experiment – “ciscyc.5km.epica.ts.10a.nc” and other netcdf files: The output of PISM sensitivity experiments, from Seguinot. (2020). Cordilleran ice sheet glacial cycle simulations continuous variables [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3606536. Click the link to see the documentation of these files. “temperature timeseries.xlsx”: the temperature forcing used in the sensitivity experiments. “PISM sensitivity.r”: R script used to analyse the PISM sensitivity experiments, including data binning, lag correlation and regression between ice sheeting response and temperature forcing. “PISM sensitivity.xlsx”: Output of the R script. “GOA.calibration.csv”: SST record from the Gulf of Alaska site 85JC/U1419, calibrated using bayspline. “GOA.Ensemble.csv”: 1000 ensemble output of the bayspline calibration. “predict ice volume SST.r”: R script used to predict the response of CIS ice volume to GOA SST forcing. The script will call the results of PISM sensitivity experiments in “PISM sensitivity.xlsx” and the GOA SST forcing in “GOA.calibration.csv” and “GOA.calibration.csv”. “PISM ice vol GOA SST.csv”: predicted PISM ice vol based on GOA SST forcing and taking into account all sensitivity experiments and the uncertainty in SST reconstruction. “PISM ice vol GOA SST model.csv”: predicted PISM ice vol based on GOA SST forcing based on each sensitivity experiment and the uncertainty in SST reconstruction. Please cite the following studies when using the data, in addition to citing the present study: GOA age model, IRD and MAR: Walczak, M. H. et al. Phasing of millennial-scale climate variability in the Pacific and Atlantic Oceans. Science 370, 716–720 (2020). Velle, J. H. et al. High resolution inclination records from the Gulf of Alaska, IODP Expedition 341 Sites U1418 and U1419. Geophys. J. Int. 229, 345–358 (2022). Heaton, T. J. et al. Marine20—The Marine Radiocarbon Age Calibration Curve (0–55,000 cal BP). Radiocarbon 62, 779–820 (2020). GOA SST: Praetorius, S. K. et al. North Pacific deglacial hypoxic events linked to abrupt ocean warming. Nature 527, 362–366 (2015). Romero, O. E., LeVay, L. J., McClymont, E. L., Müller, J. & Cowan, E. A. Orbital and Suborbital-Scale Variations of Productivity and Sea Surface Conditions in the Gulf of Alaska During the Past 54,000 Years: Impact of Iron Fertilization by Icebergs and Meltwater. Paleoceanogr. Paleoclimatology 37, e2021PA004385 (2022). Tierney, J. E. & Tingley, M. P. BAYSPLINE: A New Calibration for the Alkenone Paleothermometer. Paleoceanogr. Paleoclimatology 33, 281–301 (2018). GOA benthic foraminifera assemblage: Belanger, C. L., Sharon, Du, J., Payne, C. R. & Mix, A. C. North Pacific deep-sea ecosystem responses reflect post-glacial switch to pulsed export productivity, deoxygenation, and destratification. Deep Sea Res. Part Oceanogr. Res. Pap. 164, 103341 (2020). Sharon, Belanger, C., Du, J. & Mix, A. Reconstructing Paleo-oxygenation for the Last 54,000 Years in the Gulf of Alaska Using Cross-validated Benthic Foraminiferal and Geochemical Records. Paleoceanogr. Paleoclimatology 36, e2020PA003986 (2021). GOA productivity: Romero, O. E., LeVay, L. J., McClymont, E. L., Müller, J. & Cowan, E. A. Orbital and Suborbital-Scale Variations of Productivity and Sea Surface Conditions in the Gulf of Alaska During the Past 54,000 Years: Impact of Iron Fertilization by Icebergs and Meltwater. Paleoceanogr. Paleoclimatology 37, e2021PA004385 (2022). Addison, J. A. et al. Productivity and sedimentary δ15N variability for the last 17,000 years along the northern Gulf of Alaska continental slope. Paleoceanography 27, PA1206 (2012). GOA bulk sediment neodymium isotopes: Du, J., Haley, B. A., Mix, A. C., Walczak, M. H. & Praetorius, S. K. Flushing of the deep Pacific Ocean and the deglacial rise of atmospheric CO 2 concentrations. Nat. Geosci. 11, 749–755 (2018). GOA volcanic endmember data compilation: Cameron, C. E., Snedigar, S. F. & Nye, C. J. Alaska Volcano Observatory geochemical database. DDS 8 http://www.dggs.alaska.gov/pubs/id/29120 (2014) doi:10.14509/29120. Sarbas, B., Jochum, K. P., Nohl, U. & Hofmann, A. W. GEOROC, the MPI geochemical rock database: a new tool for geochemists. Eos Trans. AGU 80, F1184 (1999). Global and regional volcanic eruption data compilation: Huybers, P. & Langmuir, C. Feedback between deglaciation, volcanism, and atmospheric CO2. Earth Planet. Sci. Lett. 286, 479–491 (2009). Global Volcanism Program, 2013. Volcanoes of the World, v. 4.8.7. 10.5479/si.GVP.VOTW4-2013. (2013). Bryson, R. U., Bryson, R. A. & Ruter, A. A calibrated radiocarbon database of late Quaternary volcanic eruptions. EEarth Discuss 1, 123–134 (2006). Watt, S. F. L., Pyle, D. M. & Mather, T. A. The volcanic response to deglaciation: Evidence from glaciated arcs and a reassessment of global eruption records. Earth-Sci. Rev. 122, 77–102 (2013). Crosweller, H. S. et al. Global database on large magnitude explosive volcanic eruptions (LaMEVE). J. Appl. Volcanol. 1, 4 (2012). Cameron, C. E., Snedigar, S. F. & Nye, C. J. Alaska Volcano Observatory geochemical database. DDS 8 http://www.dggs.alaska.gov/pubs/id/29120 (2014) doi:10.14509/29120. Praetorius, S. et al. Interaction between climate, volcanism, and isostatic rebound in Southeast Alaska during the last deglaciation. Earth Planet. Sci. Lett. 452, 79–89 (2016). Wilcox, P. S. et al. A new set of basaltic tephras from Southeast Alaska represent key stratigraphic markers for the late Pleistocene. Quat. Res. 92, 246–256 (2019). Davies, L. J., Jensen, B. J. L., Froese, D. G. & Wallace, K. L. Late Pleistocene and Holocene tephrostratigraphy of interior Alaska and Yukon: Key beds and chronologies over the past 30,000 years. Quat. Sci. Rev. 146, 28–53 (2016). GIA models: Roy, K. & Peltier, W. R. Relative sea level in the Western Mediterranean basin: A regional test of the ICE-7G_NA (VM7) model and a constraint on late Holocene Antarctic deglaciation. Quat. Sci. Rev. 183, 76–87 (2018). Lambeck, K., Purcell, A. & Zhao, S. The North American Late Wisconsin ice sheet and mantle viscosity from glacial rebound analyses. Quat. Sci. Rev. 158, 172–210 (2017). PISM sensitivity experiments and temperature forcing: Seguinot, J., Rogozhina, I., Stroeven, A. P., Margold, M. & Kleman, J. Numerical simulations of the Cordilleran ice sheet through the last glacial cycle. The Cryosphere 10, 639–664 (2016). Seguinot. (2020). Cordilleran ice sheet glacial cycle simulations continuous variables [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3606536 Dansgaard, W. et al. Evidence for general instability of past climate from a 250-kyr ice-core record. Nature 364, 218–220 (1993). Andersen, K. K. et al. High-resolution record of Northern Hemisphere climate extending into the last interglacial period. Nature 431, 147–151 (2004). Jouzel, J. et al. Orbital and Millennial Antarctic Climate Variability over the Past 800,000 Years. Science 317, 793–796 (2007). Petit, J. R. et al. Climate and atmospheric history of the past 420,000 years from the Vostok ice core, Antarctica. Nature 399, 429–436 (1999). Herbert, T. D. et al. Collapse of the California Current During Glacial Maxima Linked to Climate Change on Land. Science 293, 71–76 (2001). Be10 data compilation: Lesnek, A. J., Briner, J. P., Baichtal, J. F. & Lyles, A. S. New constraints on the last deglaciation of the Cordilleran Ice Sheet in coastal Southeast Alaska. Quat. Res. 96, 140–160 (2020). Haeussler, P. J. et al. Late Quaternary deglaciation of Prince William Sound, Alaska. Quat. Res. 1–20 (2021) doi:10.1017/qua.2021.33. Walcott, C. K., Briner, J. P., Baichtal, J. F., Lesnek, A. J. & Licciardi, J. M. Cosmogenic ages indicate no MIS 2 refugia in the Alexander Archipelago, Alaska. Geochronology 4, 191–211 (2022). Briner, J. P. et al. The last deglaciation of Alaska. Cuad. Investig. Geográfica 43, 429–448 (2017

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Jeu de données · Signal consensuel: Jeu de données
Score de désaccord entre enseignants0,183
Score d'incertitude au seuil0,365

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,002
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0110,002

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,012
Tête enseignante GPT0,200
Écart entre enseignants0,188 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreJeu de données

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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