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Record W4255453770 · doi:10.1017/cbo9781107286221.015

Boreal Forests

2015· book-chapter· en· W4255453770 on OpenAlexaboutno aff
Derek Eamus, Alfredo Huete, Qiang Yu

Bibliographic record

VenueCambridge University Press eBooks · 2015
Typebook-chapter
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsnot available
Fundersnot available
KeywordsTaigaBorealLarchEvergreenForestryDeciduousGeographyEcologyEnvironmental sciencePhysical geographyAgroforestryBiologyArchaeology

Abstract

fetched live from OpenAlex

Introduction Boreal (meaning northern) forests occupy a circumpolar belt (Fig. 14.1) in the northern hemisphere. They are mostly dominated by evergreen needle-leaved species, including pines ( Pinus ), spruce ( Picea ), and fir ( Abies ), but also the deciduous conifer larch ( Larix ), and cover about 17 percent of the earth's land surface. They are a dominant land cover in Russia, Canada and northern Europe. In Russia boreal forests are called Taiga. An example of maximum, minimum, and mean temperatures, day length, and total precipitation (rainfall plus snowfall) for a Canadian boreal forest is given in Figure 14.2. Because boreal forests are found in high latitudes, winter temperatures are very low, summer temperatures are low-to-moderate, snowfall occurs every year, and day length varies substantially between seasons (Fig. 14.2). The growing season is confined to late spring, summer, and early autumn (100–200 days long). Where high elevation mountain ridges extend south from high latitude regions, boreal forest can also extend south–for example in New Jersey and the Appalachians in the United States. Boreal forests store approximately double the amount of carbon found in rainforests. Coping with Freezing Winters: Photosynthetic C Gain and Transpiration Consideration of the fluxes of C and water in boreal forests requires an understanding of how evergreen trees tolerate the stresses associated with very low winter temperatures. There are three major stresses that evergreen trees in boreal forests need to tolerate. The first is freezing temperatures and this stress induces two additional stresses: water stress and photo-inhibition. When air and soil temperatures are sub-zero for prolonged periods, the potential for the formation of ice within cells is high. When water freezes it expands and this ruptures living cells and xylem. When the ice melts, the ruptured cell is dead (and the xylem is dysfunctional). Therefore intra-cellular freezing must be avoided in boreal trees. As autumnal temperatures decline and day length shortens, evergreen trees undergo a process of hardening whereby the temperature that can induce freezing damage progressively declines (that is, frost hardiness increases). Accumulation of solutes within cells depresses the freezing point of intra-cellular water by several degrees but this is insufficient protection; more important are the poorly understood biochemical changes that prevent intra-cellular ice nucleation. By preventing ice nucleation cells can tolerate temperatures as low as −38°C, which is the spontaneous nucleation temperature of water.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.060
Threshold uncertainty score0.201

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0600.021

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 head0.038
GPT teacher head0.208
Teacher spread0.170 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2015
Admission routes1
Has abstractyes

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