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Record W2160827541 · doi:10.1155/2010/964260

Emerging Themes in the Ecology and Management of North American Forests

2010· article· en· W2160827541 on OpenAlexaff
Terry L. Sharik, William Adair, Fred A. Baker, Mike A. Battaglia, Emily J. Comfort, Anthony W. D’Amato, Craig DeLong, R. Justin DeRose, Mark J. Ducey, Mark E. Harmon, Louise Levy, Jesse A. Logan, Joseph J. O’Brien, Brian J. Palik, Scott D. Roberts, Paul C. Rogers, Douglas J. Shinneman, Thomas A. Spies, Sarah Taylor, Christopher W. Woodall, Andrew Youngblood

Bibliographic record

VenueInternational Journal of Forestry Research · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsGovernment of British Columbia
Fundersnot available
KeywordsEcologyForest ecologyEnvironmental resource managementDisturbance (geology)EcosystemTemporal scalesForest managementSession (web analytics)Field (mathematics)Ecosystem managementClimate changeGeographyEnvironmental scienceComputer scienceBiology

Abstract

fetched live from OpenAlex

The 7th North American Forest Ecology Workshop, consisting of 149 presentations in 16 oral sessions and a poster session, reflected a broad range of topical areas currently under investigation in forest ecology and management. There was an overarching emphasis on the role of disturbance, both natural and anthropogenic, in the dynamics of forest ecosystems, and the recognition that legacies from past disturbances strongly influence future trajectories. Climate was invoked as a major driver of ecosystem change. An emphasis was placed on application of research findings for predicting system responses to changing forest management initiatives. Several “needs” emerged from the discussions regarding approaches to the study of forest ecosystems, including (1) consideration of variable spatial and temporal scales, (2) long-term monitoring, (3) development of universal databases more encompassing of time and space to facilitate meta-analyses, (4) combining field studies and modeling approaches, (5) standardizing methods of measurement and assessment, (6) guarding against oversimplification or overgeneralization from limited site-specific results, (7) greater emphasis on plant-animal interactions, and (8) better alignment of needs and communication of results between researchers and 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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.533

Codex and Gemma teacher scores by category

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

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 head0.017
GPT teacher head0.345
Teacher spread0.327 · 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.

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

Citations27
Published2010
Admission routes1
Has abstractyes

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