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Record W2017121543 · doi:10.1139/b08-086

Substrate effects on distribution, biomass allocation, and morphology of forest understory plants

2008· article· en· W2017121543 on OpenAlexvenueno aff
Laura J. Six, Charles B. Halpern

Bibliographic record

VenueBotany · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsnot available
Fundersnot available
KeywordsUnderstoryBiologyBiomass (ecology)Forest floorCoarse woody debrisSubstrate (aquarium)MossSeedlingEcologyBotanyAgronomyHabitatEcosystemCanopy

Abstract

fetched live from OpenAlex

Relationships between rooting substrate and the distribution and performance of forest plants are inadequately understood. We tested whether understory species in a dense coniferous forest were associated with coarse woody debris (CWD) or forest floor. In addition, for three species with differing substrate associations ( Vaccinium parvifolium Smith, Tiarella trifoliata L., and Maianthemum dilatatum (Wood) Nels. and Macbr., we excavated individuals rooted in CWD and forest floor, and compared biomass allocation and plant morphological traits. Substrate samples were also tested for moisture content. Of 29 species tested, 18 (62%) showed positive associations with forest floor and 6 (21%) with CWD. Forest floor is a more predictable and stable substrate; in these forests it also supports lower moss cover that can inhibit seedling establishment. As expected, plants rooted in forest floor (which was drier) allocated greater biomass to belowground structures. Root-system traits, however, did not suggest plasticity in response to resource availability. Instead, the physical structure of logs may constrain root systems in CWD. In addition, total plant biomass did not differ between substrates suggesting that under low light, species may be incapable of responding to differences in belowground resources. Alternatively, substrate associations may develop earlier in the life histories of these plants via differential germination and survival.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.188

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 head0.021
GPT teacher head0.192
Teacher spread0.172 · 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

Citations15
Published2008
Admission routes1
Has abstractyes

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