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Record W4311082164 · doi:10.17348/jbrit.v16.i2.1270

Effects of shading on the rare plant species, Physostegia correllii (Lamiaceae) and Trillium texanum (Melanthiaceae)

2022· article· en· W4311082164 on OpenAlexaff
Beth A. Middleton, Casey R. Williams, Chris Doffitt, Darren Johnson

Bibliographic record

VenueJournal of the Botanical Research Institute of Texas · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBotany, Ecology, and Taxonomy Studies
Canadian institutionsAg-West Bio (Canada)
Fundersnot available
KeywordsShadingVegetation (pathology)Threatened speciesFloodplainEcologyBiologyForestryHabitatGeography

Abstract

fetched live from OpenAlex

Rare plant species that are constrained by shading may be threatened by a lack of natural disturbance that removes overhanging vegetation. The original distribution of the study species Physostegia correllii (Lundell) Shinners included freshwater floodplains of large rivers in the southcentral U.S. (Colorado, Rio Grande, and Mississippi rivers). A second species, Trillium texanum Buckley was found in seep spring baygalls in east-central Texas and extreme northwestern Louisiana. Experiments to determine the effects of shading on P. correllii and T. texanum were conducted using short-term shade cloth treatments (full sunlight vs. 30% shading for 2–3 weeks), and a dryness treatment for T. texanum (moist vs. less moist). Mean height and cover responses of individuals for both species were determined in conservation gardens located in Lafayette, Louisiana. Physostegia correllii grown in shaded environments for 2.5 weeks had shorter mean height than if grown in full sunlight. Half of the shaded plants in shaded plots had died by the mid-summer. For T. texanum, shading reduced the mean height and cover of plants. Therefore, management to remove overhanging ground vegetation to mimic natural disturbance might revive P. correllii and/or T. texanum populations where overhanging vegetation is increasing due to lack of natural disturbance (e.g., flood pulsing, grazing, burning).

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)0.002
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 score0.547
Threshold uncertainty score0.575

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.068
GPT teacher head0.272
Teacher spread0.204 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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