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The Evolution of AVHRR-Derived Water Temperatures over Lakes in the Mackenzie Basin and Hydrometeorological Applications

2003· article· en· W2059014173 on OpenAlexaff
Normand Bussières, William M. Schertzer

Bibliographic record

VenueJournal of Hydrometeorology · 2003
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsImpactCanadian Hydrographic Service
Fundersnot available
KeywordsEnvironmental scienceHydrometeorologyAdvanced very-high-resolution radiometerWater cycleDrainage basinHydrology (agriculture)Annual cycleStructural basinAtmospheric sciencesSatelliteClimatologyGeologyPrecipitationMeteorologyGeographyGeomorphology

Abstract

fetched live from OpenAlex

The temperature evolution of water bodies is determined and compared over the Mackenzie River hydrological basin. The thermal IR observations used to determine the water temperatures were extracted from NOAA's Advanced Very High Resolution Radiometer (AVHRR) satellite data over the period from April 1999 to September 1999. The IR temperatures were calibrated and adjusted to account for the intervening atmosphere. For each day, 1-km-resolution temperature scenes were generated. From the temperature scenes, clear-sky temperature values were extracted for water bodies with areas larger than 100 km2. The temperature cycle over water bodies can be decomposed into a small positive slope from near 0°C until the water temperature reaches 4°C, followed by a quadratic trend that can be easily fitted. The quadratic curve fit parameters give information for cataloging and comparing each water body's seasonal temperature cycle. Data stratification confirms a strong latitudinal influence on the shape of the curves. Compared to Great Slave Lake, the duration of open water for Lake Athabasca is longer and begins about 16 days earlier; for Great Bear Lake, the cycle is shorter and begins about 45 days later. Fitted maximum temperatures are 15.5°C for Lake Athabasca, 13.7°C for Great Slave Lake, and 6.8°C for Great Bear Lake. The AVHRR-derived seasonal temperatures should be useful in estimating total lake evaporation because lakes with longer and warmer seasonal temperature cycles should tend to evaporate over longer time periods than those with shorter and cooler temperature cycles.

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)0.002
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 score0.054
Threshold uncertainty score0.240

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 studies0.0000.000
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 head0.006
GPT teacher head0.207
Teacher spread0.200 · 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

Citations24
Published2003
Admission routes1
Has abstractyes

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