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Record W4385755244 · doi:10.1139/cjb-2023-0069

Returning mistletoe to an urban forest: a restoration success story

2023· article· en· W4385755244 on OpenAlexvenueno aff
David M. Watson, Melinda Cook, Rodney van der Ree, Lee R. Harrison

Bibliographic record

VenueBotany · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Parasitism and Resistance
Canadian institutionsnot available
Fundersnot available
KeywordsBiologySeedlingEcologyHost (biology)HabitatBotanyAgroforestry

Abstract

fetched live from OpenAlex

Mistletoes have a fraught public relations history. Vilified as toxic tree-killing weeds, they have long been targeted for removal by arborists and forest managers concerned with tree health. Research on mistletoe ecology has revealed their positive influence on diversity and community structure via complex networks of interactions. In areas where mistletoe has traditionally been removed, managers are now asking whether these parasitic plants can return on their own or whether they should be reintroduced. To evaluate the practicalities of mistletoe restoration, we inoculated 28 London plane trees Platanus × acerfolia (Aiton) Willd. in inner Melbourne with creeping mistletoe Muellerina eucalyptoides (DC.) Barlow, a locally rare species. Branch dimensions and aspect had no discernible effect on germination or seedling emergence, but trees where access to possums ( Trichosurus vulpecula (Kerr, 1792) and Pseudocheirus peregrinus (Boddaert, 1785)) was restricted were 30% more likely to host seedlings. Five years after inoculation, seven mistletoes successfully established on five trees, with two mistletoes bearing fruit. Communications with residents and city users elicited positive feedback, catalysing four additional inoculation trials in urban and forest settings elsewhere. In addition to representing a cost-effective means of enhancing habitat values, mistletoe restoration is useful for addressing open questions about mistletoe ecology and educating the community about the functional roles these parasitic plants play.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.875
Threshold uncertainty score0.260

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.024
GPT teacher head0.246
Teacher spread0.222 · 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

Citations10
Published2023
Admission routes1
Has abstractyes

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