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Record W2485158535 · doi:10.1002/2016gl069946

SMAP soil moisture drying more rapid than observed in situ following rainfall events

2016· article· en· W2485158535 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueGeophysical Research Letters · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil Moisture and Remote Sensing
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Guelph
FundersCanadian Space AgencyAgricultural Research ServiceGoddard Space Flight CenterEnvironment CanadaU.S. Department of AgricultureNational Aeronautics and Space Administration
KeywordsIn situEnvironmental scienceWater contentMoistureSoil scienceAtmospheric sciencesMeteorologyGeologyGeotechnical engineering

Abstract

fetched live from OpenAlex

Abstract We examine soil drying rates by comparing surface soil moisture observations from the NASA Soil Moisture Active Passive (SMAP) mission to those from networks of in situ probes upscaled to SMAP's sensing footprint. SMAP and upscaled in situ probes record different soil drying dynamics after rainfall. We modeled this process by fitting an exponential curve to 63 drydown events: the median SMAP drying timescale is 44% shorter and the magnitude of drying is 35% greater than in situ measurements. We also calculated drying rates between consecutive observations from 193 events. For 6 days after rainfall, soil moisture from SMAP dries at twice the rate of in situ measurements. Restricting in situ observations to times of SMAP observations does not change the drying timescale, magnitude, or rate. Therefore, observed differences are likely due to differences in sensing depths: SMAP measures shallower soil moisture than in situ probes, especially after rainfall.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.924
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.036
GPT teacher head0.292
Teacher spread0.256 · 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