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Supplementary material to "A numerical model study of the main factors contributing to hypoxia and its sub-seasonal to interannual variability off the Changjiang Estuary"

2019· preprint· en· W4237993647 on OpenAlexaff
Haiyan Zhang, Katja Fennel, Arnaud Laurent, Changwei Bian

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsDalhousie University
Fundersnot available
KeywordsEstuaryHypoxia (environmental)Environmental scienceOceanographyFisheryBiologyGeologyChemistryOxygen

Abstract

fetched live from OpenAlex

Model-data comparisons of temperature and salinityThe model reproduces remotely sensed spatial and temporal SST patterns (from the NOAA AVHRR sensor; https://www.nodc.noaa.gov/SatelliteData/ghrsst/)very well with monthly correlation coefficients of 0.89 and above (Figure S1).Simulated surface salinity also shows similar spatial and seasonal pattern as available in situ data (Figure S2) with a correlation coefficient of 0.84.Both the model and observations indicate that the CDW is confined to the coast south of the estuary in early spring (March 2011) and autumn (October 2013) and extends eastward and northeastward in summer.Some interannual variations occur, e.g., the CDW extends northward and eastward in June 2012 while it mainly spreads southeastward in June 2013.At the bottom, freshwater is confined to the coast with high-salinity water coming from the open ocean (Figure S3).The correlation coefficient between simulated and observed bottom salinity is 0.87.Simulated bottom temperature shows significant seasonal variations consistent with the observations (Figure S4) with a correlation coefficient of 0.88.Higher temperature in south and east regions in March indicates the commencement of Kuroshio intrusion onto shelves.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.309
Threshold uncertainty score0.986

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.3090.019

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 head0.015
GPT teacher head0.235
Teacher spread0.221 · 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.

Study designSimulation or modeling
Domainnot available
GenreDataset

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

Citations1
Published2019
Admission routes1
Has abstractyes

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