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Record W7162842797

Quantifying Novel Ecosystems

2025· other· en· W7162842797 on OpenAlexaff
Rosie Bibby

Bibliographic record

VenueWhite Rose eTheses Online (University of Leeds, The University of Sheffield, University of York) · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsYork University
Fundersnot available
KeywordsNoveltyAbiotic componentContext (archaeology)Principal (computer security)Scale (ratio)EcosystemTemporal scalesBiodiversity
DOInot available

Abstract

fetched live from OpenAlex

The concept of “novel ecosystems” is becoming increasingly prominent in the scientific literature concerning ecology and conservation in the Anthropocene. However, the literature reveals several inconsistent and qualitative framings of the “novel ecosystems” concept, hindering systematic efforts to study or address novel ecosystems. This dissertation quantifies novelty in ecosystems and develops a methodology to identify and predict the emergence of novel ecosystems in dynamic landscapes. Previous attempts to define novelty have not been widely accepted in the scientific community, impeding its practical application in conservation. The project assessed previous methods to identify their strengths and weaknesses, aiming to develop a new method for successfully quantifying novelty. Building on the findings from the previous methods, two rigorous metrics were developed. Firstly, we tested a method based on calculating dissimilarity between variables associated with novelty at two time periods to get a total novelty score. A second method using Euclidean distance in principal components analysis (PCA) was developed to measure temporal and spatial novelty by calculating distances between points in PCA space. Crucially, both methods involve biotic and abiotic factors. Applying these metrics in the United Kingdom (UK) context using data from the British Trust for Ornithology (BTO), AVONET, the UK Centre for Ecology and Hydrology (UK CEH) and the Centre for Environmental Data Analysis (CEDA) across a time scale spanning 1968 to 2011 showed how abiotic and biotic novelty do not reflect the same spatial or temporal patterns. This is demonstrated in another application using data from the Global Biodiversity Information Facility (GBIF) and lepidoptera traits, showing the method’s reproducibility and reliability. Overall, I advocate for the use and further development of the PCA method to quantify ecological novelty, incorporating both abiotic and biotic variables whilst maintaining flexibility in its application to different scenarios.

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)0.003
metaresearch head score (Gemma)0.014
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.005
Science and technology studies0.0010.002
Scholarly communication0.0050.008
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.229
Teacher spread0.191 · 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
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

Citations0
Published2025
Admission routes1
Has abstractyes

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