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Record W2175081775 · doi:10.1175/jam2265.1

Measuring Canopy Structure and the Kinematics of Subcanopy Flows in Two Forests

2005· article· en· W2175081775 on OpenAlexaboutno aff
Ralf M. Staebler, David R. Fitzjarrald

Bibliographic record

VenueJournal of Applied Meteorology · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsGeologyTerrainBuoyancyCanopyFlow (mathematics)Canopy interceptionEcologyThroughfallMechanicsSoil sciencePhysics

Abstract

fetched live from OpenAlex

Abstract A better understanding of forest subcanopy flows is needed to evaluate their role in the horizontal movement of scalars, particularly in complex terrain. This paper describes detailed measurements of the canopy structure and its variability in both the horizontal and vertical directions at a deciduous forest in complex terrain (the Harvard Forest, Petersham, Massachusetts). The effects of the trunks and subcanopy shrubs on the flow field at each of six subcanopy array locations are quantified. The dynamics of the subcanopy flow are examined with pragmatic methods that can be implemented on a small scale with limited resources to estimate the stress divergence, buoyancy, and pressure gradient forces that drive the flow. The subcanopy flow at the Harvard Forest was driven by mechanisms other than vertical stress divergence 75% of the time. Nocturnal flows were driven predominantly by the negative buoyancy of a relatively cool layer near the forest floor. The direction of the resulting drainage flows followed the azimuth of the longest forest-floor slope. Similar results were found at a much flatter site at Borden, Ontario, Canada. There was no clear evidence of flow reversals in the subcanopy in the lee of ridges or hills at the Harvard Forest even in high wind conditions, contrary to some model predictions.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.328
Threshold uncertainty score0.182

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)0.0000.000

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 head0.007
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 designSimulation or modeling
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

Citations63
Published2005
Admission routes1
Has abstractyes

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