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Record W3006253835 · doi:10.1029/2020gl087100

Experimental Estimates of Optical Backscattering Associated With Submicron Particles in Clear Oceanic Waters

2020· article· en· W3006253835 on OpenAlexaff
Xiaodong Zhang, Lianbo Hu, Yuanheng Xiong, Yannick Huot, Deric J. Gray

Bibliographic record

VenueGeophysical Research Letters · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsUniversité de Sherbrooke
FundersNational Aeronautics and Space AdministrationNational Science Foundation
KeywordsScatteringBackscatter (email)Coherent backscatteringRange (aeronautics)Materials scienceParticle (ecology)Trophic levelMineralogyAnalytical Chemistry (journal)OpticsOceanographyPhysicsChemistryGeologyEnvironmental chemistryComposite material

Abstract

fetched live from OpenAlex

Abstract The volume scattering functions for bulk and two submicron size fractions (passing through 0.7 μm GF/F and 0.2 μm membrane filters) were measured in the North Pacific Ocean using a LISST‐VSF to assess the contributions by submicron particles to the overall particle scattering. The contribution by submicron particles increased generally with scattering angle and peaked around 110°. The total backscattering by both submicron fractions did not vary, but that by larger particles increased, with the trophic level. Consequently, the fractional backscattering contribution by submicron particles decreased from approximately 50% to 30% as the chlorophyll‐a concentration increased from <0.1 to 0.3 mg m−3. Despite the experiment covering a limited range of trophic levels, our results confirm that backscattering by submicron particles in clear ocean waters is significant and seems to form a background independent of the backscattering by larger particles.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.035
GPT teacher head0.262
Teacher spread0.227 · 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 designBench or experimental
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

Citations33
Published2020
Admission routes1
Has abstractyes

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