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Record W3177520391 · doi:10.5194/egusphere-egu21-11184

The SPLASH Action Group – Towards standardized sampling strategies along the soil-to-hydrosystems continuum in permafrost landscapes

2021· article· en· W3177520391 on OpenAlexaff
Julien Fouché, Sarah Shakil, Catherine Hirst, Lisa Bröder, Yannick Agnan, Ylva Sjöberg, Frédéric Bouchard

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversité LavalCenter for Northern StudiesUniversity of Alberta
Fundersnot available
KeywordsPermafrostBiogeochemical cycleEnvironmental scienceSoil waterArcticHydrology (agriculture)Soil scienceOceanographyGeologyEnvironmental chemistryChemistryGeotechnical engineering

Abstract

fetched live from OpenAlex

The Action Group called ‘Standardized methods across Permafrost Landscapes: from Arctic Soils to Hydrosystems’ (SPLASH), funded by the International Permafrost Association, is a community-driven effort aiming to provide a suite of standardized field strategies for sampling mineral and organic components in soils, sediments, surface water bodies and coastal environments across permafrost landscapes. This unified approach will allow data to be shared and compared, thus improving our understanding of the processes occurring during lateral transport in circumpolar Arctic watersheds. This is an international and transdisciplinary effort aiming to provide a fieldwork “tool box” of the most relevant sampling schemes and sample conservation procedures for mineral and organic permafrost pools. With climate change, permafrost soils are undergoing drastic transformations. Both localized abrupt thaw (thermokarst) and gradual ecosystem shifts (e.g., active layer thickening, vegetation changes) drive changes in hydrology and biogeochemical cycles (carbon, nutrients, and contaminants). Mineral and organic components interact along the “lateral continuum” (i.e., from soils to aquatic systems) changing their composition and reactivity across the different interfaces. The circumpolar Arctic region is characterized by high spatial heterogeneity (e.g., geology, topography, vegetation, and ground-ice content) and large inter-annual and seasonal variations in local climate and biophysical processes. Common sampling strategies, applied in different seasons and locations, could help to tackle the spatial and temporal complexity inextricably linked to biogeochemical processes. This unified approach developed in permafrost landscapes will allow us to overcome the following challenges: (1) identifying interfaces where detectable changes in mineral and organic components occur; (2) allowing spatial comparison of these detectable changes; and (3) capturing temporal (inter-/intra-annual) variations at these interfaces. In order to build on the great effort to better assess the permafrost feedback to climate change, there is an urgent need for a set of community-based protocols to capture changes the dynamics of organics and minerals during their lateral transport. Here, we present the first results from an online survey recently conducted among researchers from different disciplines. The survey inputs provide valuable information about the common approaches currently applied along the “soil-to-hydrosystems” continuum and the specific challenges associated with permafrost studies. These results about the ‘WHAT, WHERE, WHEN, and HOW’ of field sampling (e.g., sample collection, filtration, conservation...) allow for identifying the most relevant sampling strategies and also the current knowledge gaps. Finally, we present examples of the protocols available to investigate organic and mineral components from soils to marine environments, on which a synoptic sampling strategy can be built. All forthcoming contributions from our community are still welcome, helping the SPLASH team to fill up the most adapted tool box to Arctic permafrost landscapes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.025
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.025
Threshold uncertainty score0.131

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0160.007

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 head0.060
GPT teacher head0.293
Teacher spread0.233 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations0
Published2021
Admission routes1
Has abstractyes

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