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Record W2123694424 · doi:10.1111/1365-2745.12195

Looking forward through the past: identification of 50 priority research questions in palaeoecology

2013· article· en· W2123694424 on OpenAlexaff
Alistair W. R. Seddon, Anson W. Mackay, Ambroise Baker, H. J. B. Birks, Elinor Breman, Caitlin E. Buck, Erle C. Ellis, Cynthia A. Froyd, Jacquelyn L. Gill, Lindsey Gillson, Edward A. Johnson, Vivienne J. Jones, Steve Juggins, Marc Macias‐Fauria, Keely Mills, J.L. Morris, David Nogués‐Bravo, Surangi W. Punyasena, Thomas P. Roland, Andrew J. Tanentzap, Martin Aberhan, Eline N. van Asperen, William E. N. Austin, Rick Battarbee, Shonil Bhagwat, Christina L. Belanger, K. D. Bennett, Hilary H. Birks, Christopher Bronk Ramsey, Stephen J. Brooks, Mark de Bruyn, Paul Butler, Frank M. Chambers, Stewart J. Clarke, Althea L. Davies, John A. Dearing, Thomas H. G. Ezard, Angelica Feurdean, Roger J. Flower, Peter Gell, Sonja Hausmann, Erika J. Hogan, Melanie J. Hopkins, Elizabeth S. Jeffers, Atte Korhola, Rob Marchant, Thorsten Kiefer, Mariusz Lamentowicz, Isabelle Larocque‐Tobler, Lourdes López Merino, Lee Hsiang Liow, Suzanne McGowan, Joshua H. Miller, Encarni Montoya, Oliver Morton, Sandra Nogué, Chloe Onoufriou, Lisa Park Boush, Francisco Rodríguez‐Sánchez, Neil L. Rose, Carl D. Sayer, Helen Shaw, Richard J. Payne, Gavin L. Simpson, Kadri Sohar, Nicki J. Whitehouse, John W. Williams, Andrzej Witkowski

Bibliographic record

VenueJournal of Ecology · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of ReginaYork UniversityUniversity of Calgary
FundersNatural Environment Research CouncilSight Research UK
KeywordsPaleoecologyAnthropoceneWildnessBiodiversityEcologyEnvironmental resource managementAnimal ecologyBiologyEnvironmental science

Abstract

fetched live from OpenAlex

Summary Priority question exercises are becoming an increasingly common tool to frame future agendas in conservation and ecological science. They are an effective way to identify research foci that advance the field and that also have high policy and conservation relevance. To date, there has been no coherent synthesis of key questions and priority research areas for palaeoecology, which combines biological, geochemical and molecular techniques in order to reconstruct past ecological and environmental systems on time‐scales from decades to millions of years. We adapted a well‐established methodology to identify 50 priority research questions in palaeoecology. Using a set of criteria designed to identify realistic and achievable research goals, we selected questions from a pool submitted by the international palaeoecology research community and relevant policy practitioners. The integration of online participation, both before and during the workshop, increased international engagement in question selection. The questions selected are structured around six themes: human–environment interactions in the Anthropocene; biodiversity, conservation and novel ecosystems; biodiversity over long time‐scales; ecosystem processes and biogeochemical cycling; comparing, combining and synthesizing information from multiple records; and new developments in palaeoecology. Future opportunities in palaeoecology are related to improved incorporation of uncertainty into reconstructions, an enhanced understanding of ecological and evolutionary dynamics and processes and the continued application of long‐term data for better‐informed landscape management. Synthesis. Palaeoecology is a vibrant and thriving discipline, and these 50 priority questions highlight its potential for addressing both pure (e.g. ecological and evolutionary, methodological) and applied (e.g. environmental and conservation) issues related to ecological science and global change.

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)0.075
metaresearch head score (Gemma)0.139
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.925
Threshold uncertainty score0.396

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0750.139
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0130.014
Science and technology studies0.0050.007
Scholarly communication0.0140.012
Open science0.0030.011
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0080.001

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 head0.039
GPT teacher head0.337
Teacher spread0.298 · 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.

Study designTheoretical or conceptual
DomainMethods
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

Citations260
Published2013
Admission routes1
Has abstractyes

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