From soils to clouds - An integrated atmospheric boundary layer observatory in a temperate forest of eastern Canada
Bibliographic record
Abstract
Temperate forests have been shown to substantially impact near-surface climate and atmospheric boundary layer dynamics through a range of complex land-atmosphere feedback mechanisms. For example, forests can reduce water loss to the atmosphere during periods of high vapour pressure deficit, thereby preventing or delaying severe drought impacts. Reducing water loss during periods of high atmospheric water demand comes at the expense of reduced forest productivity and may contribute to additional warming of near-surface air temperatures through increased partitioning of energy to sensible heat. Understanding how land-atmosphere interactions in forested landscapes modify regional and local climate is thus crucial for the design of efficient national and international climate mitigation and adaptation strategies.To better understand complex land-atmosphere interactions in a typical forested landscape of eastern Canada, we have established an integrated atmospheric boundary layer observatory in a temperate forest in New Brunswick, Canada. Observations will be used to quantify environmental, plant physiological, and atmospheric feedbacks and their impacts on near-surface climate. Here, we present the instrumental setup and preliminary results from the integrated observatory. Forest soils are monitored using soil temperature, volumetric soil moisture, soil water potential, and snow depth measurements and are complemented by soil CO2 efflux measurements using forced diffusion chamber systems. Detailed vertical profiles of air temperature and humidity, wind speed and direction, and light are measured from the forest floor to a height of 28 m (i.e., 18 m above the forest canopy) using six weather stations. At the top of the flux tower at 28 m above ground, net ecosystem CO2 exchange and evapotranspiration of the forested landscape is measured using the eddy covariance technique along with longwave and shortwave radiation fluxes. A ceilometer will be added to the observatory in spring 2023 to continuously observe cloud base height and atmospheric boundary layer height. The integrated measurements will produce datasets that can be used to diagnose complex land-atmosphere interactions, to characterise forest microclimate, and to validate coupled land-atmosphere models.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".