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SOLIDS DISTRIBUTION IN LAKE CHAPALA, MEXICO<sup>1</sup>

2004· article· en· W2046634804 on OpenAlexaff
Harvey Shear, Ulrich Maniak, Pedro F. Zdrate‐del Valle

Bibliographic record

VenueJAWRA Journal of the American Water Resources Association · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsTotal dissolved solidsTributaryEnvironmental scienceSuspended solidsWater qualityHydrology (agriculture)Total suspended solidsSedimentationVolume (thermodynamics)SedimentEnvironmental engineeringGeologyGeographyWastewaterEcology

Abstract

fetched live from OpenAlex

ABSTRACT: Lake Chapala is the largest natural water body in Mexico and also one of the most important shallow lakes in Latin America. For the past several years it has suffered various environmental problems such as the upstream overuse of water, contamination, and sedimentation. For the past 10 years the lake has had less than 50 percent of its historical water level over the past century. No criteria are reported in the literature that establish a water storage volume that will guarantee water quality conditions necessary for the survival of the lake. After determining the behavior of total solids concentrations in relation to the variations in the lake's depth, we proposed a minimum water column height of 5.0 m, representing a storage volume of about 5,000 Mm 3 . This volume would result in the recommended water quality standards for total dissolved solids. Calculated distribution maps show that the total solids concentration in the lake has increased since the end of the 1970s. The solids are primarily concentrated in the eastern part of the lake, as a consequence of the high solids discharged from the Lerma River, its main tributary.

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)0.000
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 score0.213
Threshold uncertainty score0.392

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.234
Teacher spread0.226 · 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

Citations15
Published2004
Admission routes1
Has abstractyes

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