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Record W4281764185 · doi:10.3390/biology11060842

Aerosol Nutrients and Their Biological Influence on the Northwest Pacific Ocean (NWPO) and Its Marginal Seas

2022· review· en· W4281764185 on OpenAlexaff
Cui Guo, Yao Zhou, Hongyan Zhou, Chang Su, Liangliang Kong

Bibliographic record

VenueBiology · 2022
Typereview
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsMcGill University
FundersNational Natural Science Foundation of ChinaUniversity of Tasmania
KeywordsAerosolBiogeochemical cycleNutrientDeposition (geology)PhytoplanktonEnvironmental scienceSeawaterOceanographyBiologyAtmospheric sciencesEnvironmental chemistryEcologySedimentGeographyMeteorologyGeologyChemistry

Abstract

fetched live from OpenAlex

Atmospheric deposition is recognized as a significant source of nutrients in the surface ocean. The East Asia region is among the largest sources of aerosol emissions in the world, due to its large industrial, agricultural, and energy production. Thus, East Asian aerosols contain a large proportion of anthropogenic particles that are characterized by small size, complex composition, and high nutrient dissolution, resulting in important influences on marine microbes and biogeochemical cycles in the downwind areas of the northwest Pacific Ocean (NWPO). By using remote sensing, modeling, and incubation experimental methods, enhanced primary production due to the East Asian aerosol input has been observed in the NWPO, with subsequent promotion and inhibition impacts on different phytoplankton taxa. Changes of bacterial activity and diversity also occur in response to aerosol input. The impact of East Asian aerosol loadings is closely related to the amount and composition of the aerosol deposition as well as the hydrological condition of the receiving seawater. Here, we review the current state of knowledge on the atmospheric nutrients and the effects of the East Asian aerosols on microbes in the NWPO region. Future research perspectives are also proposed.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.049
GPT teacher head0.243
Teacher spread0.194 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations9
Published2022
Admission routes1
Has abstractyes

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