MétaCan
Menu
Back to cohort
Record W2071734206 · doi:10.5751/es-05924-180445

Understanding Adaptive Capacity in Forest Governance: Editorial

2013· article· en· W2071734206 on OpenAlexvenueno aff
E. Carina H. Keskitalo

Bibliographic record

VenueEcology and Society · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
FundersSvenska Forskningsrådet Formas
KeywordsAdaptive capacityClimate changeVulnerability (computing)Flooding (psychology)Context (archaeology)Environmental resource managementPsychological resilienceCorporate governanceAdaptation (eye)Environmental planningPolitical scienceBusinessGeographyEconomicsEcologyComputer sciencePsychologySocial psychology

Abstract

fetched live from OpenAlex

"The term adaptive capacity has often been used to indicate the role that various factors may play in determining the extent to which adaptation to climate change - different actions to deal with the consequences of climate change - is possible. While the focus on adaptive capacity has been pronounced within climate change literature, this literature strongly acknowledges that adaptation will not take place with regard to climate change alone. Adaption to climate change should rather be seen in the context of adaption to all other coexisting stressors, or what has been called double or multiple impacts. The social, economic and political situation thus plays a part in determining whether environmental impact or exposure will result in vulnerability and in consequences on the ground. For instance, a flood will only become a disaster if the preparedness needed to deal with the consequences of flooding does not exist. The adaptive capacity or resources to deal with the risk of flooding, such as the existence of emergency plans and the existence of funding and personnel, are crucial."

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.530
Threshold uncertainty score0.999

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)0.0020.000

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 head0.035
GPT teacher head0.217
Teacher spread0.182 · 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.

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

Citations11
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueEcology and SocietySame topicForest Management and PolicyFrench-language works237,207