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Record W1736229063 · doi:10.5962/bhl.title.48203

Geological Character and Mineral Resources of South Central Lake Erie

2018· book· en· W1736229063 on OpenAlexfundno aff
S. Jeffress Williams, Edward P. Meisburger

Bibliographic record

VenueUS Army Corps of Engineers: Engineer Research and Development Center (Knowledge Core) · 2018
Typebook
Languageen
FieldEnvironmental Science
TopicWater Quality and Resources Studies
Canadian institutionsnot available
FundersCanada Excellence Research Chairs, Government of Canada
KeywordsGeologyPeninsulaMineral resource classificationGeological surveyArchaeologyGeomorphologyGeochemistryPaleontologyGeography

Abstract

fetched live from OpenAlex

During the summers of 1977 and 1978, a 900-square kilometer region of southern Lake Erie, between the Ohio-Pennsylvania border and Erie, Pennsylvania, was surveyed, using high resolution seismic reflection equipment and long vibracores, to determine the shallow subbottom geologic character of the lake floor. Emphasis was placed on describing the sediments and identifying deposits of sand and gravel that might be dredged and used as fill for beach nourishment projects on Presque Isle Peninsula. A total of 416 kilometers of seismic profiles and 49 cores with an average length of 4.1 meters were analyzed along with 23 grab samples.

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 score0.076
Threshold uncertainty score0.150

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.080
GPT teacher head0.268
Teacher spread0.187 · 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

Citations8
Published2018
Admission routes1
Has abstractyes

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