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Record W4393007704 · doi:10.1038/s41597-024-03139-w

The Arctic Plant Aboveground Biomass Synthesis Dataset

2024· article· en· W4393007704 on OpenAlexafffund
Logan T. Berner, Kathleen M. Orndahl, Melissa Rose, Mikkel P. Tamstorf, Marie Frost Arndal, Heather D. Alexander, Elyn Humphreys, M. M. Loranty, S. Ludwig, Johanna Nyman, Sari Juutinen, Mika Aurela, Konsta Happonen, Juha Mikola, Michelle C. Mack, Mathew R. Vankoughnett, Colleen M. Iversen, Verity Salmon, Dedi Yang, Jitendra Kumar, Paul Grogan, Ryan K. Danby, Neal A. Scott, Johan Olofsson, Matthias Siewert, Lucas Deschamps, Esther Lévesque, Vincent Maire, Amélie Morneault, Gilles Gauthier, Charles Gignac, Stéphane Boudreau, Anna Gaspard, Alexander Kholodov, M. Syndonia Bret‐Harte, Heather E. Greaves, Donald A. Walker, Fiona Gregory, Anders Michelsen, Timo Kumpula, Miguel Villoslada, Henni Ylänne, Miska Luoto, Tarmo Virtanen, Bruce C. Forbes, Norbert Hölzel, Howard E. Epstein, Ramona Julia Heim, Andrew G. Bunn, R. M. Holmes, Jacqueline K. Y. Hung, Susan M. Natali, Anna‐Maria Virkkala, S. J. Goetz

Bibliographic record

VenueScientific Data · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversity of AlbertaUniversité LavalQueen's UniversityAlberta Biodiversity Monitoring InstituteNova Scotia Community CollegeUniversité du Québec à Trois-RivièresCarleton University
FundersBiological and Environmental ResearchDivision of Environmental BiologyFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaOffice of ScienceSocietas pro Fauna et Flora FennicaNordenskiöld-samfundetAcademy of FinlandQueen's UniversityU.S. Department of EnergyEuropean CommissionOffice of Polar ProgramsPolar Knowledge CanadaNuclear Safety and Security CommissionUniversité LavalArcticNetNatural Resources CanadaNational Aeronautics and Space AdministrationVetenskapsrådetNational Science Foundation
KeywordsBiomass (ecology)Environmental scienceThe arcticArcticEcologyOceanographyBiologyGeology

Abstract

fetched live from OpenAlex

Abstract Plant biomass is a fundamental ecosystem attribute that is sensitive to rapid climatic changes occurring in the Arctic. Nevertheless, measuring plant biomass in the Arctic is logistically challenging and resource intensive. Lack of accessible field data hinders efforts to understand the amount, composition, distribution, and changes in plant biomass in these northern ecosystems. Here, we present The Arctic plant aboveground biomass synthesis dataset , which includes field measurements of lichen, bryophyte, herb, shrub, and/or tree aboveground biomass (g m −2 ) on 2,327 sample plots from 636 field sites in seven countries. We created the synthesis dataset by assembling and harmonizing 32 individual datasets. Aboveground biomass was primarily quantified by harvesting sample plots during mid- to late-summer, though tree and often tall shrub biomass were quantified using surveys and allometric models. Each biomass measurement is associated with metadata including sample date, location, method, data source, and other information. This unique dataset can be leveraged to monitor, map, and model plant biomass across the rapidly warming Arctic.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.232
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0030.001
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.006

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.108
GPT teacher head0.287
Teacher spread0.178 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations14
Published2024
Admission routes2
Has abstractyes

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