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Record W6987398455

Structural characterization of dissolved organic matter in permafrost peatland lakes

2020· article· en· W6987398455 on OpenAlexaboutno aff

Bibliographic record

VenueRepositório da Universidade de Lisboa (University of Lisbon) · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
Fundersnot available
KeywordsThermokarstPermafrostPeatDissolved organic carbonBogOrganic matterBayBiogeochemistry
DOInot available

Abstract

fetched live from OpenAlex

Thermokarst lakes result from the thawing of ice-rich permafrost and are widespread across northern landscapes. These waters are strong emitters of methane, especially in permafrost peatland regions, where they are stained black by high concentrations of dissolved organic matter (DOM). In the present study, we aimed to structurally characterize the DOM from a set of peatland thermokarst lakes that are known to be intense sites of microbial decomposition and methane emission. Samples were collected at different depths from three thermokarst lakes in the Sasapimakwananisikw (SAS) River valley near the eastern Hudson Bay community of Kuujjuarapik–Whapmagoostui (Nunavik, Canada). Samples were analyzed by spectrofluorometry, Fourier-transform infrared spectroscopy (FTIR), nuclear magnetic resonance spectroscopy (NMR), and elemental analysis. Fluorescence analyses indicated considerable amounts of autochthonous DOM in the surface waters of one of SAS 1A, indicating a strong bioavailability of labile DOM, and consequently a greater methanogenic potential. The three lakes differed in their chemical composition and diversity, suggesting various DOM transformations phenomena. The usefulness of complementary analytical approaches to characterize the complex mixture of DOM in permafrost peatland waters cannot be overlooked, representing a first step towards greater comprehension of the organic geochemical properties of these permafrost-derived systems

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

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 head0.018
GPT teacher head0.185
Teacher spread0.167 · 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 teacher head, not a consensus.

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
Published2020
Admission routes1
Has abstractyes

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