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Record W2789534798 · doi:10.1080/11956860.2018.1435064

Plant community composition, tree physiology, and bark-beetle mortality in relation to open and enclosed irrigation channels: a case study within a wildland-urban interface forest in western Montana

2018· article· en· W2789534798 on OpenAlexvenueno aff
Eric G. Keeling

Bibliographic record

VenueEcoscience · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsBark beetleEnvironmental scienceEcologyChannel (broadcasting)IrrigationBasal areaBark (sound)GeographyHydrology (agriculture)BiologyGeology

Abstract

fetched live from OpenAlex

Dry coniferous forests within the wildland–urban interface (WUI) in the northwestern US have experienced long-term drought and increasing bark-beetle infestations. An unexamined factor potentially affecting WUI forests are irrigation channels used to convey water to nearby farms. I studied the effects of two irrigation channels (a downslope, open channel and an upslope, recently enclosed channel) on plant community composition, tree water status and growth, and patterns of bark-beetle mortality in a WUI ponderosa pine/Douglas fir forest experiencing drought. Low-lying swale areas below the recently enclosed, upper channel had extensive down dead wood and showed shifts from mesic understory plant species to more xeric species. Tree density and basal area declined in swale areas below both channels. Ponderosa pine below the enclosed channel showed evidence of water stress (low water potentials) and negative growth responses to the channel enclosure. In contrast, hillslope trees below the open channel had higher growth rates and water potentials compared to trees above that channel. Beetle-kill was highest between the two channels, the area most likely affected by upper channel enclosure. The study provides evidence that plant community composition, tree physiology, and patterns of tree mortality in forests may be affected by irrigation channels interacting with small-scale topography.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.259
Threshold uncertainty score0.982

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 studies0.0000.000
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 head0.026
GPT teacher head0.272
Teacher spread0.245 · 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
Published2018
Admission routes1
Has abstractyes

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