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Using Foraminifera as Indicators of Habitat Recovery following Remediation of Sheltered Tidal Flats in Saudi Arabia

2014· article· en· W2071230436 on OpenAlexaff
Brant M. Priest, Jason A. Hale, Thomas G. Minter, Christopher D. Cormack, Ion Cotsapas, Michael J. Risk

Bibliographic record

VenueInternational Oil Spill Conference Proceedings · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsEnvironmental remediationIntertidal zoneSalt marshHabitatTidal flatEnvironmental scienceIntertidal ecologySedimentDredgingEcologyGeologyHydrology (agriculture)OceanographyBiologyGeotechnical engineeringContaminationGeomorphology

Abstract

fetched live from OpenAlex

After nearly 20 years of limited natural recovery of intertidal habitats along the Gulf Coast of the Kingdom of Saudi Arabia, large-scale remediation projects were conducted on approximately 1800 ha of tidal flat and salt marsh habitat. In Fall, 2011, multiple passes of mechanical tilling were used to break up oiled cohesive sediment layer across a heavily degraded sand tidal flat, to reduce subsurface liquid oil, and accelerate natural recovery. Rates and degrees of test deformities in three foram genera were measured from samples collected at degraded and healthy sand tidal flat sites. Dominant genera and rates of test deformity at a heavily oiled sand tidal flat (average surface and subsurface total petroleum hydrocarbon (TPH) = 10,000 ppm ) were: Peneroplis (41.0%), Ammonia (38.7%), Elphidium (54.7%). Rates of deformity in the same three genera collected at a healthy sand tidal flat habitat were: Peneroplis (11.8%), Ammonia (7.5%), Elphidium (13.9%). Nearly two years after the remediation event, results indicate a decreasing trend in percent foram deformities at the remediation site, which suggests oil toxicity as an ecological stressor has decreased as a result of remediation activities.

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)0.001
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 score0.029
Threshold uncertainty score0.458

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.281
Teacher spread0.251 · 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
Published2014
Admission routes1
Has abstractyes

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