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Record W2941547353

Mixing and Deposition in a Jack Pine Forest Canopy

2018· dissertation· en· W2941547353 on OpenAlexaboutno aff
Kaiti Jiang

Bibliographic record

VenueYorkSpace (York University) · 2018
Typedissertation
Languageen
FieldEngineering
TopicVehicle emissions and performance
Canadian institutionsnot available
Fundersnot available
KeywordsCanopyDeposition (geology)TaigaEnvironmental sciencePine forestTree canopyAtmospheric sciencesAerosolSink (geography)Hydrology (agriculture)TowerForestryMeteorologyGeographyStructural basinGeologyGeomorphology
DOInot available

Abstract

fetched live from OpenAlex

To study how aerosols mix and deposit to forests, a tower was erected in a jack pine forest as part of the York Athabasca Jack Pine project. The tower is surrounded by anthropogenic pollution sources from the Alberta Oil Sands operations.
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\nFrom previous studies, we expected that canopies inhibit mixing and deposition. During the study, the air within the forest was often decoupled from the air above. Mixing at the study site took up to 40 minutes during periods where the canopy was decoupled, compared to less than 2 minutes when the canopy was coupled.
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\nAt different times during the campaign, the forest was either a sink or a source of aerosols. The mean aerosol deposition velocity, an important parameter used by deposition models, was measured in this boreal forest. A local minimum of v_d (with respect to particle diameter) of 0.16 cm/s was observed at D = 150 nm.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.381
Threshold uncertainty score0.957

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 head0.006
GPT teacher head0.182
Teacher spread0.176 · 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 designNot applicable
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

Citations0
Published2018
Admission routes1
Has abstractyes

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