MétaCan
Menu
Back to cohort
Record W4307882834 · doi:10.15273/pnsis.v52i2.11496

Sydney Harbour: Seiches, tides and mean circulation

2022· article· en· W4307882834 on OpenAlexvenueno aff
Brian Petrie

Bibliographic record

VenueProceedings of the Nova Scotian Institute of Science · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
Fundersnot available
KeywordsSeicheHarbourInflowGeologyOceanographySea levelEstuaryCirculation (fluid dynamics)Current (fluid)ClimatologyPhysicsMechanics

Abstract

fetched live from OpenAlex

Beginning with observations from 1901, sea level elevations and currents from Sydney Harbour are examined across a broad frequency range. The mean currents, annual components of sea level, tides and seiches, mainly in the South Arm, are the focus. Tidal and mean currents are ~0.01 m s-1. The general circulation is estuarine-like with a thin, near-surface outflow layer and a thicker, deeper inflow. The distribution of contaminants in bottom sediments suggests the circulation, though weak, plays a retentive role in the Arm, transporting sediments towards its head. Analysis of 11-years of sea level data indicates a strong annual cycle, more energetic during winter than from late spring to early fall. The increased energy occurred at all frequencies except for tides. Seiches, with periods of ~0.5 to 2 h, emerge as a strong contributor to sea level and currents. The distributions of elevation and flow amplitudes associated with seiches were derived. With maximum observed values of 0.74 m and 0.24 m s-1, seiche displacements and currents can exceed those associated with tides and the mean circulation. While earlier studies identified only the dominant fundamental seiche mode, recent sea level data sampled at 1-minute show that modes 2-4 occur. Keywords: Sydney Harbour, sea level, mean circulation, tides, seiches

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.164
Threshold uncertainty score0.327

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.242
Teacher spread0.220 · 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 designObservational
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

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Nova Scotian Institute of ScienceSame topicMarine and fisheries researchFrench-language works237,207