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Record W4293007975 · doi:10.11159/icepr22.127

Water Quality Assessment of Keelung Port

2022· article· en· W4293007975 on OpenAlexvenueno aff
Erdy Aganus, Jung‐Chang Wang, Chia-An Yang, Nathalia Quintero

Bibliographic record

VenueProceedings of the World Congress on New Technologies · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsPort (circuit theory)Quality (philosophy)Computer scienceEnvironmental scienceEngineering

Abstract

fetched live from OpenAlex

This research aimed to conduct water quality assessment in the port area covered by the Keelung Port in Keelung City, Taiwan and to determine if spatial variability and tidal variability affect the water quality parameters. This research addresses the gap on the limited work published on the pollutants present and dissolved in the water. Relatively little attention has been given to the study area, where different shipping operations may have considerable impacts on the water quality in the port environment. Based on the standards set by the Taiwan International Ports Corporation (TIPC), pH and DO are within the permissible values. DO was markedly lower during high tide, which was consistent with the findings of other researchers. The concentration of mercury, arsenic, and chromium were all below the detection limits of the measuring equipment used in the research. Water quality is significantly affected by the location of sampling area, whereas the inner port area showed relatively higher level of contamination compared with the measurements taken at the outer port area. Based on the independent samples Mann-Whitney U test conducted, tidal condition does not significantly affect the distribution of measurements of the water quality parameters.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.528
Threshold uncertainty score0.851

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.033
GPT teacher head0.303
Teacher spread0.270 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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