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Record W4394247836 · doi:10.6084/m9.figshare.12630101

Dissolved oxygen and carbonate system in a southern nearshore marine aquaculture area in the North Yellow Sea in May, June, July, August and September 2017

2020· dataset· en· W4394247836 on OpenAlexaboutno aff
Chenglong Li, Weidong Zhai

Bibliographic record

VenueFigshare · 2020
Typedataset
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsOceanographyCarbonateFisheryAquacultureGeographyGeologyEnvironmental scienceFish <Actinopterygii>BiologyChemistry

Abstract

fetched live from OpenAlex

Around the North Yellow Sea, Liaoning and Shandong Provinces in China are involved in fast-developing and highly populated marine aquacultural activities. For example, its southern nearshore waters are occupied by an important mariculture area in Shandong, where the water depth is only 10−21 m. Sea cucumbers <i>Apostichopus japonicus</i>, and scallops <i>Chlamys Farreri</i>, are cultured in this area where summertime hypoxia and pH decline have been observed in 2015 and 2016.<br>From May to September, five southern nearshore surveys (monthly from May to September) were carried out in the North Yellow Sea in 2017. During the nearshore surveys, discrete water samples were collected using a 5-L Niskin bottle at the surface (2 m below sea surface) and 2 m above the seafloor, while depth profiles of temperature and salinity (practical salinity scale of 1978) were determined using a calibrated RBR profiler (RBR Ltd., Canada). <br>Sampling surveys were supported by the Yantai Institute of Coastal Zone Research, the Chinese Academy of Sciences, via the Strategic Priority Research Program of the Chinese Academy of Sciences (grant no. XDA11020702).<br>

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.108
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.040
GPT teacher head0.243
Teacher spread0.204 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations0
Published2020
Admission routes1
Has abstractyes

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