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Record W2048018174 · doi:10.1002/iroh.200410766

Physical and Chemical Limnology of 34 Lentic Waterbodies along a Tropical-to-Alpine Altitudinal Gradient in Nepal

2005· article· en· W2048018174 on OpenAlexafffund
P. Lacoul, Bill Freedman

Bibliographic record

VenueInternational Review of Hydrobiology · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaDalhousie UniversityGovernment of the United Kingdom
KeywordsLimnologyEutrophicationLake ecosystemAltitude (triangle)Trophic state indexEnvironmental scienceWatershedHydrology (agriculture)Effects of high altitude on humansTotal suspended solidsEcologyEcosystemNutrientGeologyGeographyBiology

Abstract

fetched live from OpenAlex

Physical and chemical factors were studied in 34 lentic waterbodies distributed along a steep altitudinal gradient ranging from tropical (77 m) to high alpine (up to 4,980 m) environments in Nepal. Bicarbonate and calcium were dominant among anions and cations, respectively, reflecting a strong influence of carbonate weathering and watershed area, rather than altitudinal climate. The relative patterns of dominant ions were similar among lakes in all altitudinal regions, although total concentrations increased with decreasing altitude. Total suspended solids were relatively high in the study lakes, as is also typical of rivers in the Ganges watershed. Suspended solids had a greater influence on water transparency than did algal biomass in the study lakes. In general, high-altitude waterbodies were oligotrophic, while those at low altitude were eutrophic. The productivity of high-altitude study lakes appeared to be limited by both available phosphorus and nitrogen, while lowland ones were nitrogen-limited. (© 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.019
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.257
Teacher spread0.250 · 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 source (direct Gemma or distilled Codex), 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

Citations44
Published2005
Admission routes2
Has abstractyes

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