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Record W118914738 · doi:10.3189/172756401781819256

The influence of the forest on night-time snow surface temperature

2001· article· en· W118914738 on OpenAlexfundno aff
Peter Höller

Bibliographic record

VenueAnnals of Glaciology · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsnot available
FundersHealth Technology Assessment international
KeywordsSnowEmissivityAtmospheric sciencesCanopyAir temperatureEnvironmental scienceTree canopyRange (aeronautics)Hydrology (agriculture)GeologyMeteorologyGeographyPhysicsMaterials science

Abstract

fetched live from OpenAlex

Abstract Snow surface temperature (T s ) plays an important role in the formation of surface hoar or near-surface faceted crystals The goal of this study was to obtain detailed information on T s in different forest stands nelr the timberline. The investigations were conducted during clear nights and showed that the snow surface temperature is influenced very strongly by the forest canopy. While the air temperature was very similar on the different experimental sites, T s was higher in the forest than in the open field; on the south-facing slope the difference between the forest and the open field was 3–4.5°C, and on the north-facing slope approximately 3–7°C. Taking into account that ε air is 0.7 and ε tree is 0.94, the incoming radiation (I ↓) for the different experimental sites was calculated by the equation of Brunt (the canopy density was estimated using photographs taken with an 8 mm fish-eye). To calculate T s , air temperature and averaged values of the net radiation (because the net radiation (I) has only a small range of variation during clear nights) were used. The results show that the calculated values were higher than the measured values (by approximately 2°C). However, a better correlation was found by using lower values of the emissivity (ε air 0.67 and ε tree 0.91).

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)0.000
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 score0.403
Threshold uncertainty score0.143

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.010
GPT teacher head0.231
Teacher spread0.221 · 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

Citations17
Published2001
Admission routes1
Has abstractyes

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