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Record W4389562652 · doi:10.7185/gold2023.17007

Snapshots of the World’s Cold Regions Changing Biogeochemistry

2023· article· en· W4389562652 on OpenAlexaff
Philippe Van Cappellen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBiogeochemistryComputer scienceGeologyOceanography

Abstract

fetched live from OpenAlex

The Earth's cold and cold-temperate regions are undergoing deep and accelerating changes due to climate change. Warming in moderate-to high-latitude terrestrial and aquatic ecosystems is accompanied by permafrost thaw, shorter winters, reduced ice cover, earlier snowmelt, more intense soil freeze-thaw cycles, drier summers, and longer fire seasons. These environmental changes in turn impact surface water and groundwater flows, water quality, greenhouse gas emissions, soil stability, primary production, and (micro)biological communities. Warming also facilitates agricultural expansion, urban growth, and natural resource development, adding growing human pressures to cold regions' water resources, soil health, and biodiversity. In this presentation, I will provide some snapshots that illustrate several of the challenges, knowledge gaps, and opportunities in cold region biogeochemical research, with an emphasis on processes and responses in both natural and anthropic environments. Many of the biogeochemical changes are closely interrelated with the changing hydrological and thermal regimes affecting cold regions' landscapes and water bodies. Compared with their temperate counterparts, these landscapes and water bodies experience shorter growing seasons, more persistent ice and snow covers, extensive permafrost, pronounced cycles of freezing and thawing, and intensifying physical and chemical weathering. Here, I will focus on some of the more unique features and response dynamics of cold regions' biogeochemistry. In addition to new unpublished work, my presentation will cover material from [1][2][3][4].

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.008
GPT teacher head0.199
Teacher spread0.191 · 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 designObservational
Domainnot available
GenreEmpirical

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
Published2023
Admission routes1
Has abstractyes

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