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Record W6894119422 · doi:10.5281/zenodo.7125404

Western Boreal Initiative Interim Workshop

2020· article· en· W6894119422 on OpenAlexaffabout

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsEnvironment and Climate Change CanadaNatural Resources CanadaCanadian Forest Service
Fundersnot available
KeywordsClimate changeTaigaBorealEcosystem servicesForest managementInterimWoodlandStakeholder

Abstract

fetched live from OpenAlex

Modelling cumulative effects (CE) implies that we can measure and forecast current and future consequences of multiple stressors such as wildfires, pests, forestry and other anthropogenic disturbances, and climate change on the ecosystem services supplied by Canada’s forests. As the number of natural and human-caused disturbances change, CE require evaluation of land and forest management interactions, while also assessing the implications of shifting prioritization among more than one forest value (e.g., woodland caribou, other Species at Risk (SAR), timber supply, carbon, and downstream economic values). This is a critical issue for all jurisdictions in Canada and requires large multidisciplinary and collaborative efforts based on sound scientific and socio-economic research. This Western Boreal Initiative represents an ambitious spatial expansion and diversification of forest values from a pilot project in Northwest Territories (Micheletti et al., 2019, 2021; Stewart et al., in review). The project will integrates the best available data, meta-modeling tools, a diverse array of domain experts, and ongoing stakeholder engagement to evaluate the cumulative effects of wildfire, key pests, and anthropogenic disturbances, and climate change on forest values in the Western Boreal Forests of Canada. Working with scientists, foresters, provincial and territorial governments, Indigenous Peoples, and other stakeholders, we will provide forecasts of future forest conditions and interpretations of ecological and socio-economic indicators, as well as how these compare to various thresholds and targets. When these indicators are forecasted to pass their respective thresholds and targets, we will quantify the trade-offs with other values, and attempt to identify solutions through optimization that maximize synergies and minimize negative trade-offs. Key values we will evaluate are economic values related to forestry, conservation values of established protected areas, National Parks, Species at Risk (including boreal and woodland caribou), Environment and Climate Change Canada priority places and the implication on forest carbon. This work will support multi-species management objectives and the Pan Canadian Framework on Clean Growth and Climate Change. At the highest level, our objective has been to build a powerful and generic toolkit for forest, species, and land management under changing future conditions that we apply to the Western Boreal Forest region of Canada. This toolkit is built with flexible, interoperable, scientifically-based models and data and allow for a new generation of integrated answers to ongoing management questions. The management context and paradigms that we are working within includes Cumulative Effects, The Pan-Canadian Framework on Clean Growth and Climate Change, Sustainable Forest Management, Multi-species Management, and Indigenous Co-production and Knowledge. We divide this larger objective into a series of sub-objectives that will address specific elements, which are discussed in the presentation.

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)0.006
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.175
Threshold uncertainty score0.428

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0060.002
Open science0.0030.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.1280.039

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 head0.250
Teacher spread0.201 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2020
Admission routes2
Has abstractyes

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