MétaCan
Menu
Back to cohort
Record W2018606128 · doi:10.1029/2012jc008158

Ganga‐Brahmaputra river discharge from Jason‐2 radar altimetry: An update to the long‐term satellite‐derived estimates of continental freshwater forcing flux into the Bay of Bengal

2012· article· en· W2018606128 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

VenueJournal of Geophysical Research Atmospheres · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsCentennial College
Fundersnot available
KeywordsBayForcing (mathematics)BENGALDischargeAltimeterStandard deviationHydrology (agriculture)Environmental scienceFlux (metallurgy)ClimatologyGeologyOceanographyGeographyDrainage basinRemote sensingStatisticsCartographyMathematics

Abstract

fetched live from OpenAlex

This paper discusses the use of Jason‐2 radar altimeter measurements to estimate the Ganga‐Brahmaputra surface freshwater flux into the Bay of Bengal for the period mid‐2008 to December 2011. A previous estimate was generated for 1993–2008 using TOPEX‐Poseidon, ERS‐2 and ENVISAT, and is now extended using Jason‐2. To take full advantages of the new availability of in situ rating curves, the processing scheme is adapted and the adjustments of the methodology are discussed here. First, using a large sample of in situ river height measurements, we estimate the standard error of Jason‐2–derived water levels over the Ganga and the Brahmaputra to be respectively of 0.28 m and 0.19 m, or less than ∼4% of the annual peak‐to‐peak variations of these two rivers. Using the in situ rating curves between water levels and river discharges, we show that Jason‐2 accurately infers Ganga and Brahmaputra instantaneous discharges for 2008–2011 with mean errors ranging from ∼2180 m 3 /s (6.5%) over the Brahmaputra to ∼1458 m 3 /s (13%) over the Ganga. The combined Ganga‐Brahmaputra monthly discharges meet the requirements of acceptable accuracy (15–20%) with a mean error of ∼16% for 2009–2011 and ∼17% for 1993–2011. The Ganga‐Brahmaputra monthly discharge at the river mouths is then presented, showing a marked interannual variability with a standard deviation of ∼12500 m 3 /s, much larger than the data set uncertainty. Finally, using in situ sea surface salinity observations, we illustrate the possible impact of extreme continental freshwater discharge event on the northern Bay of Bengal as observed in 2008.

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)0.002
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 score0.208
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.305
Teacher spread0.287 · 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