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Record W2077321343 · doi:10.1139/x10-058

Habitat preferences of an arboreal forage lichen in a Sierra Nevada old-growth mixed-conifer forest

2010· article· en· W2077321343 on OpenAlexvenueno aff
Thomas R. Rambo

Bibliographic record

VenueCanadian Journal of Forest Research · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLichen and fungal ecology
Canadian institutionsnot available
Fundersnot available
KeywordsSubarctic climateArboreal locomotionBiomass (ecology)Environmental scienceEcologyForestryBotanyBiologyHabitatGeography

Abstract

fetched live from OpenAlex

The arboreal forage lichen Bryoria fremontii (Tuck.) Brodo & D.Hawksw. appears sensitive to conditions of prolonged hydration in wet forests of British Columbia. I estimated the abundance of this lichen in mixed-conifer forest canopy in the contrasting Mediterranean climate of the southern Sierra Nevada in relationship to the vertical gradient of vapor pressure deficit. Abundance was estimated by biomass in 5 m strata from the ground to the tops of 50 trees. Transplants of Bryoria thalli were installed in 18 fir trees for 1 year to assess their growth relative to distance from perennial streams. VPD generally increased with height, being significantly greater at 45 m than 5 m. Bryoria biomass averaged across tree heights was estimated as 15.9, 0.60, 0.15, 0.25, and 0.19 g·m –1 in red fir (Abies magnifica A.Murray), white fir (Abies concolor (Gordon & Glend.) Hildebr. var. lowiana (Gordon) Lemmon), incense cedar (Calocedrus decurrens (Torr.) Florin), Jeffrey pine (Pinus jeffreyi Balf.), and sugar pine (Pinus lambertiana Douglas), respectively. Transplant growth was significantly greater in trees <25 m from water (11.4%) than in trees >25 m from water (9.7%). Bryoria had strong positive associations with red fir, proximity to streams, and decreasing VPD. The sensitivity of Bryoria to prolonged hydration does not appear to be important in this climate characterized by extended summer dryness.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.890
Threshold uncertainty score0.912

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.039
GPT teacher head0.280
Teacher spread0.241 · 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

Citations16
Published2010
Admission routes1
Has abstractyes

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