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Record W4386842847 · doi:10.2138/gselements.19.1.22

Making Salt from Water: The Unique Mineralogy of Alkaline Lakes

2023· article· en· W4386842847 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueElements · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPrecipitationMineralSupersaturationGeologyGeochemistryAqueous solutionSalt (chemistry)MineralogySeawaterEnvironmental chemistryChemistryOceanography

Abstract

fetched live from OpenAlex

Alkaline lakes have some of the most unique and diverse known mineral assemblages as a result of their very high pH and dissolved inorganic carbon concentrations. In these closed-basin systems, aqueous geochemistry and mineralogy are intimately linked, whereby the removal of elements through mineral precipitation controls the lake water geochemistry. The resulting extreme water chemistry of alkaline lakes produces minerals that are rare in other environments, including low-temperature minerals that record valuable environmental information and that are commonly extracted as mineral resources. Alkaline lakes are also excellent environments to study various processes in mineral crystallization, growth, and transformation, including the formation of metastable phases, precipitation after extreme supersaturation, co-precipitation of minerals, and the influence of dynamic conditions.

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.

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 score0.286
Threshold uncertainty score0.998

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)0.0030.000

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 head0.028
GPT teacher head0.250
Teacher spread0.222 · 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