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Record W4416379930 · doi:10.1002/fee.70016

Overcoming barriers that limit the impact of ecological research

2025· article· en· W4416379930 on OpenAlexafffund
Carlos Cano‐Barbacil, James F. Cahill, Helen M. Regan, Talya D. Hackett, Jacob N. Barney, Isabel Donoso, Franz Essl, Emili García‐Berthou, Tina Heger, Lotte Korell, Ingolf Kühn, Demetra Rákosy, Kristiina Visakorpi, Núria Roura‐Pascual

Bibliographic record

VenueFrontiers in Ecology and the Environment · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Ecology, Wildlife Education
Canadian institutionsUniversity of Alberta
FundersLeibniz-GemeinschaftNatural Sciences and Engineering Research Council of CanadaHORIZON EUROPE Framework ProgrammeDirectorate for Biological SciencesThünen-InstitutFreie Universität BerlinBiodiversa+Euskal Herriko UnibertsitateaAgencia Estatal de InvestigaciónTechnische Universität MünchenUniversität WienDeutsche ForschungsgemeinschaftIkerbasque, Basque Foundation for ScienceUniversity of LeedsNorges Teknisk-Naturvitenskapelige UniversitetUniversity of OxfordUniversitat de Girona
KeywordsPerceptionNatural (archaeology)Limit (mathematics)BiodiversityEcological systems theory

Abstract

fetched live from OpenAlex

Ecology and conservation researchers have diverse goals that often include both personal career aspirations and desires to enhance the well‐being of the natural world and its inhabitants. Perception of ecological research by ecologists typically involves a triad—linking goals, research, and impact. Yet the realities of scientific practice are substantially more complicated due to numerous constraints that limit the ability of researchers to conduct ecological research and to have a genuine impact. Many of these barriers can be mitigated, leading to more effective contributions to society and biodiversity conservation. Here, we outline frequently encountered constraints in ecological research institutions and, by drawing upon many practices used internationally, we identify feasible mitigations and highlight examples of negative consequences that can occur in the absence of effective mitigation strategies. Finally, we propose changes to aspects of the culture and reward systems that would allow ecological research as a discipline to more effectively achieve societal, environmental, and personal goals.

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)0.003
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 score0.019
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.289
Teacher spread0.269 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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