MétaCan
Menu
Back to cohort
Record W4283360230 · doi:10.1029/2022gb007304

Extreme Nitrate Deficits in the Western Arctic Ocean: Origin, Decadal Changes, and Implications for Denitrification on a Polar Marginal Shelf

2022· article· en· W4283360230 on OpenAlexaboutno aff
Yanpei Zhuang, Haiyan Jin, Wei‐Jun Cai, Hongliang Li, Di Qi, Jianfang Chen

Bibliographic record

VenueGlobal Biogeochemical Cycles · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsNitrateOceanographyDenitrificationArcticEnvironmental scienceCanada BasinClimatologyGeologyNitrogenEcologyChemistryBiology

Abstract

fetched live from OpenAlex

Abstract The western Arctic Ocean is known to be nitrate deficient relative to phosphate but the decadal trend and the processes contributing to the deficit are not clear. To investigate changes in this extreme nitrate deficit of over 10 μmol/kg and its causal mechanisms, nutrient concentrations were examined along a transect spanning the Bering Basin, the Bering–Chukchi Shelf, and the western Arctic Ocean Basin over the last two decades (1994–2018). The results show that the extreme nitrate deficit has extended to greater depths and further north during the past two decades, which coincided with the expansion of Pacific water in the western Arctic Ocean. Subsurface nutrient stocks in the basin areas appear to have increased, but are accompanied by a larger nitrate deficit, which may be due to stronger shelf denitrification. This nitrate loss (∆N) caused by shelf denitrification was estimated to be 7.3 ± 0.1 μmol/kg during the interval 2012–2018, which was ∼10% higher than that in 1994. This suggests an intensification of denitrification on this marginal shelf under climatic and environmental change in the Arctic Ocean.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.448

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.246
Teacher spread0.219 · 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.

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

Citations26
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueGlobal Biogeochemical CyclesSame topicArctic and Antarctic ice dynamicsFrench-language works237,207