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Record W2942903851 · doi:10.1007/s13595-019-0827-x

Forest adaptation to climate change—is non-management an option?

2019· article· en· W2942903851 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAnnals of Forest Science · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsForest managementClimate changeEnvironmental resource managementForest restorationAdaptation (eye)BusinessEnvironmental scienceForest ecologyAgroforestryEcologyEcosystemPsychology

Abstract

fetched live from OpenAlex

Abstract Key message Climate change is posing a considerable challenge to foresters. The intensity of required adaptive measures and the relevance of old-growth forests as benchmark for managed forests are debated. Forest managers need to make decisions on stand treatment that are based on climatological and biological parameters with high uncertainties. We provided the conceptual basis for adaptive forest management and provide a number of case studies that reflect the options and limitations of ways of coping with climate change. The examples are derived from the experience of the authors. We conclude that only few forest types are either not strongly affected by climate change or do not require immediate adaptations of forest management. Many productive forests have stand properties that are decisively shaped by past management decisions, such as tree species composition, age distribution, rotation period, and stand structure. Maintaining these properties under the influence of climate change requires continuous and even increasing efforts of forest managers.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.254
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.005

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 head0.049
GPT teacher head0.320
Teacher spread0.272 · 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