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Record W4281659002 · doi:10.1029/2022pa004466

Memory Effects in Salinity Profiles From Black Sea Sediments

2022· article· en· W4281659002 on OpenAlexafffund
Bernard P. Boudreau, Stephen E. Calvert, Markus Kienast

Bibliographic record

VenuePaleoceanography and Paleoclimatology · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and environmental studies
Canadian institutionsUniversity of British ColumbiaDalhousie University
FundersDalhousie University
KeywordsSalinityBrackish waterGeologyOceanographyHoloceneSapropelGlacial periodInterglacialSedimentary rockSaline waterMarine transgressionStructural basinGeochemistryPaleontologyMediterranean climateEcology

Abstract

fetched live from OpenAlex

Abstract The current saline state the Black Sea is only the latest of a series of freshening‐salinization episodes that have affected that body of water during past glacial‐interglacial cycles. Here, we model the salinity history of the basin and its sedimentary porewaters since the end of the penultimate saline period, variously thought to have occurred in the period between 128 and 65 Kyrs BP (Before Present). Our results argue that the down‐core salinity profiles of Black Sea Holocene sediments have been affected by upward diffusion of salt from the penultimate saline episode and possibly from residual salinity of the basin waters captured in the accumulating porewaters. Our retrodictions require that the Black Sea bottom waters be either fresh or weakly brackish ( S ≤ 4), between 80 and 10 Kyrs BP. In addition, we find that the timing of the first deposition of the Holocene sapropel corresponds to the time when salty bottom waters first reached the surface waters, and we speculate that increased organic matter production was caused by the release of nutrients stored in the saline bottom water. Finally, using current salinity proxy data, we find that the porewater salinity profiles generated from these proxies do not match the observed interstitial profiles.

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 categoriesInsufficient payload (model declined to judge)
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 score0.999

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 studies0.0000.000
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 head0.008
GPT teacher head0.189
Teacher spread0.181 · 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.

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 routes2
Has abstractyes

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