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Record W4415557589 · doi:10.4138/atlgeo.2025.016

A tale of two ponds, Newfoundland, Canada

2025· article· en· W4415557589 on OpenAlexaffabout
A. M. Leitch, Jianguang Chen, Harunor Rashid

Bibliographic record

VenueAtlantic Geoscience · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological formations and processes
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsSinkholeBathymetrySonarSound (geography)Ground-penetrating radarSquare (algebra)Sediment

Abstract

fetched live from OpenAlex

Bathymetric surveys of two small lakes in Newfoundland, located in different environments and separated by hundreds of kilometres, were carried out using two different survey methods—ground penetrating radar (GPR) and sound navigation and ranging (sonar). The different structures of these two disparate ponds were found to be related to the differing geology and degrees of anthropogenic influence at the two locations. In addition, the study outlined the strengths and limitations of the two survey methods. Tipping’s Pond, on the outskirts of the town of Corner Brook in western Newfoundland, is a sinkhole in a popular recreation area. It is roughly square with an area of 1.6 km2 and is slightly salty, making it largely impenetrable by radar. Bathymetric surveys with a salinity-impervious fish-finder sonar system revealed Tipping’s Pond to be bowl-shaped and more than 25 m deep in the centre. Grassy Pond, 3 km inland from the Trans-Canada Highway in eastern Newfoundland, is within an undeveloped area accessible by snowmobile in the winter. It has an irregular, elongated shape 1.2 km2 in area and is very fresh. As well as determining the bathymetry, GPR was able to determine the depth of a soft sediment layer overlying till, and to image structures within the soft sediments. The top of the sediment layer is undulating and shallow (<2.9 m deep) whereas the base of the sediments overlies sub-basins about 8 m deep.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.914

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.213
Teacher spread0.205 · 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 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

Citations0
Published2025
Admission routes2
Has abstractyes

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