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Relict shorelines and ice flow patterns of the northern puget lowland from lidar data and digital terrain modelling

2004· article· en· W1996124874 on OpenAlexaff
Dori J. Kovanen, Olav Slaymaker

Bibliographic record

VenueGeografiska Annaler Series A Physical Geography · 2004
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGeologyShoreLidarSea levelTerrainOceanographyAltitude (triangle)Physical geographyDeposition (geology)GeomorphologySedimentGeographyRemote sensingCartography

Abstract

fetched live from OpenAlex

AbstractAirborne lidar data from the northern Puget Lowland provide information on the spatial variability and amplitude of raised postglacial shorelines, marine deltaic features and glaciomarine sediments deposited between approximately c. 12 920 and 11 050 14C yr BP (15 960‐12 364 cal yr BP). Relict shorelines preserved in embayments on Whidbey and Camano islands (between 47°54′N and 48°24′N) are found up to an altitude of c. 90 m and record glacio‐isostatic movements attributed to postglacial rebound. The tilt of the regional minimum highstand sea level surface to the north of 0.80 m km−1, with local variability from 0.25 m km−1 to 0.77 m km−1, is consistent with previous studies (Thorson 1989; Dethier et al. 1995). The local variability is related to the uncertainty in the depth of the water column above these features at the time of deposition and probable tectonic deformation. The information generated by these lidar data is most valuable in posing new research questions, generating alternative research hypotheses to those already formulated in the northern Puget Lowland.Key words: sea level changerelict shorelineslidar datadigital terrain modellingpostglacial upliftPuget Lowland

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 score0.014
Threshold uncertainty score0.606

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.220
Teacher spread0.203 · 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

Citations21
Published2004
Admission routes1
Has abstractyes

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