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Record W4381163275 · doi:10.1139/er-2022-0116

Future-proofing the emergency recovery plan for freshwater biodiversity

2023· article· en· W4381163275 on OpenAlexaffvenue
Abigail J. Lynch, Amanda A. Hyman, Steven J. Cooke, Samantha J. Capon, P.A. Franklin, Sonja C. Jähnig, Matthew McCartney, Nguyễn Phú Hòa, Margaret Awuor Owuor, Jamie Pittock, Michael J. Samways, Luiz G. M. Silva, E. Ashley Steel, David Tickner

Bibliographic record

VenueEnvironmental Reviews · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsCarleton University
FundersLeibniz-GemeinschaftU.S. Geological SurveyNature Conservancy
KeywordsBiodiversityEnvironmental resource managementEnvironmental planningClimate changeBusinessFreshwater ecosystemScale (ratio)Conservation PlanHabitatEnvironmental scienceEcologyGeographyEcosystemBiology

Abstract

fetched live from OpenAlex

Freshwater biodiversity loss is accelerating globally, but humanity can change this trajectory through actions that enable recovery. To be successful, these actions require coordination and planning at a global scale. The Emergency Recovery Plan for global freshwater biodiversity aims to reduce the risk for freshwater biodiversity loss through six priority actions: (1) accelerate implementation of environmental flows; (2) improve water quality to sustain aquatic life; (3) protect and restore critical habitats; (4) manage exploitation of freshwater species and riverine aggregates; (5) prevent and control nonnative species invasions in freshwater habitats; and (6) safeguard and restore freshwater connectivity. These actions can be implemented using future-proofing approaches that anticipate future risks (e.g., emerging pollutants, new invaders, and synergistic effects) and minimize likely stressors to make conservation of freshwater biodiversity more resilient to climate change and other global environmental challenges. While uncertainty with respect to past observations is not a new concern for freshwater biodiversity, future-proofing has the distinction of accounting for the uncertainty of future conditions that have no historical baseline. The level of uncertainty with respect to future conditions is unprecedented. Future-proofing of the Emergency Recovery Plan for freshwater biodiversity will require anticipating future changes and developing and implementing actions to address those future changes. Here, we showcase future-proofing approaches likely to be successful using local case studies and examples. Ensuring that response options within the Emergency Recovery Plan are future-proofed will provide decision makers with science-informed choices, even in the face of uncertain and potentially new future conditions. We are at an inflection point for global freshwater biodiversity loss; learning from defeats and successes can support improved actions toward a sustainable future.

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)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0040.005
Open science0.0020.005
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0260.004

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 head0.027
GPT teacher head0.227
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 designTheoretical or conceptual
Domainnot available
GenreReview

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

Citations37
Published2023
Admission routes2
Has abstractyes

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