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Plant traits – a tool for restoration?

2012· article· en· W1976092738 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueApplied Vegetation Science · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsnot available
FundersCalifornia Department of Fish and WildlifeU.S. Fish and Wildlife ServiceNature ConservancyNature Conservancy of CanadaWashington Department of Fish and WildlifeOregon State University
KeywordsBiologyTraitEcologyPlant speciesRestoration ecology

Abstract

fetched live from OpenAlex

Abstract Question Most results of restoration efforts are species‐specific and/or site‐specific and therefore are not general enough to be easily applied to other species and other sites. Our research addresses the issue of species‐specific results by investigating the feasibility of using plant traits instead of taxonomic identity to characterize species responses to restoration treatments. Location Ten bunchgrass prairie sites in the P acific N orthwest of N orth A merica ( O regon and zashington, USA ; B ritish C olumbia, C anada). Methods We developed two types of quantitative models for each of ten prairie restoration sites: (1) plant trait models, which related plant traits to species field responses following restoration management treatments; and (2) species identity models, which related species taxonomic identity to species field responses following restoration management treatments. Species identity models determined the maximum amount of variability of field responses that can be explained by differences in individual species' responses to management treatments. Plant trait models determined what proportion of this explanatory power can be attributed to plant traits. The two model types addressed the following specific questions: (1) how much of the variability in field responses (changes in cover) of plants to restoration management treatments is explained by plant traits; and (2) how well do plant traits explain the variability of field responses (changes in cover) following restoration management treatments compared to models relating field responses to species identity? Results (1) The plant trait models explained much of the variability within each of the ten restoration sites, with R 2 values ranging between 31% and 69%. (2) The species identity models explained between 47% and 74% of variability of change in cover ( R 2 ). Thus, the plant trait models explained nearly as much variability as the species identity models. In seven out of nine sites, the plant trait models were superior to the species identity models, as measured by AIC , i.e. the trait models did well at explaining variability with less model complexity. Conclusion Strong explanatory power of plant trait models supports the feasibility of using plant traits instead of species taxonomic identity as a common language to characterize plant field responses (changes in cover) to restoration treatments.

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.

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)0.001
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 score0.837
Threshold uncertainty score0.530

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.017
GPT teacher head0.263
Teacher spread0.246 · 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