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Record W4400886240 · doi:10.1007/s10201-024-00756-7

Influence of environmental settings, including vegetation, on speciation of the redox-sensitive elements in the sediments of monomictic Lake Kinneret

2024· article· en· W4400886240 on OpenAlexaff
Alexey Kamyshny, Rotem Klein, Werner Eckert, Khoren Avetisyan

Bibliographic record

VenueLimnology · 2024
Typearticle
Languageen
FieldEnergy
TopicIron oxide chemistry and applications
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersBen-Gurion University of the NegevIsrael Science Foundation
KeywordsEnvironmental chemistrySedimentRedoxSulfurLittoral zoneSulfideManganesePore water pressureSulfateGeologyChemistryOceanographyGeomorphologyInorganic chemistry

Abstract

fetched live from OpenAlex

L. into the sediments on cycling of the redox-sensitive elements in Lake Kinneret. We measured roots content, porosity, and chemical parameters including pH, sulfur, iron and manganese speciation in the sediments from the shore, littoral and sublittoral zones with and without vegetation. Our results show that at ≥ 12 m water depth, the upper 10 cm of the sediments are affected by the active sulfur cycling with concentrations of hydrogen sulfide > 70 μM near the sediment-water interface. Speciation of sulfur, iron, and manganese in the upper 10 cm of littoral sediments, which are covered by < 20 cm of water, are affected by their permeability and, to a lesser extent, by roots penetration. In the case when sediments are not covered by water, oxygen penetration to the sediments by desiccation is an additional important control of the redox zonation in the surface sediments. In the shore sediments, despite relatively high concentrations of sulfate in the pore-waters, sulfur cycling may be described as "cryptic" as expressed by very low concentrations of hydrogen sulfide in the pore-waters. This is most likely a result of its fast reoxidation by the abundant highly reactive Fe(III) and Mn(IV) phases. Supplementary Information: The online version contains supplementary material available at 10.1007/s10201-024-00756-7.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.259
Threshold uncertainty score0.187

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)0.0000.000

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 head0.008
GPT teacher head0.236
Teacher spread0.227 · 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 designBench or experimental
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

Citations4
Published2024
Admission routes1
Has abstractyes

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