MétaCan
Menu
Back to cohort
Record W2138619377 · doi:10.1175/jhm-d-13-093.1

Long-Term Changes of Lake Level and Water Budget in the Nam Co Lake Basin, Central Tibetan Plateau

2014· article· en· W2138619377 on OpenAlexaff
Yanhong Wu, Hongxing Zheng, Bing Zhang, Dongmei Chen, Liping Lei

Bibliographic record

VenueJournal of Hydrometeorology · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsQueen's University
FundersChina Meteorological AdministrationChinese Academy of Sciences
KeywordsPlateau (mathematics)Environmental scienceHydrology (agriculture)Water balancePrecipitationSurface runoffClimate changeWater levelStructural basinLake ecosystemGlacierClimatologyPhysical geographyGeologyEcosystemOceanographyGeomorphologyGeographyMeteorology

Abstract

fetched live from OpenAlex

Abstract Long-term changes in the water budget of lakes in the Tibetan Plateau due to climate change are of great interest not only for the importance of water management, but also for the critical challenge due to the lack of observations. In this paper, the water budget of Nam Co Lake during 1980–2010 is simulated using a dynamical monthly water balance model. The simulated lake level is in good agreement with field investigations and the remotely sensed lake level. The long-term hydrological simulation shows that from 1980 to 2010, lake level rose from 4718.34 to 4724.93 m, accompanied by an increase of lake water storage volume from 77.33 × 109 to 83.66 × 109 m3. For the net lake level rise (5.93 m) during the period 1980–2010, the proportional contributions of rainfall–runoff, glacier melt, precipitation on the lake, lake percolation, and evaporation are 104.7%, 56.6%, 41.7%, −22.2%, and −80.9%, respectively. A positive but diminishing annual water surplus is found in Nam Co Lake, implying a continuous but slowing rise in lake level as a hydrological consequence of climate change.

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 score0.056
Threshold uncertainty score0.961

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)0.0010.000

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 head0.024
GPT teacher head0.232
Teacher spread0.208 · 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

Citations62
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of HydrometeorologySame topicCryospheric studies and observationsFrench-language works237,207