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Record W2613752810 · doi:10.1139/cjb-2016-0314

Variations in nitrogen, phosphorus, and δ<sup>15</sup>N in <i>Sphagnum</i> mosses along a climatic and atmospheric deposition gradient in eastern Canada

2017· article· en· W2613752810 on OpenAlexafffundvenueabout
Tatjana Živković, Kristina Disney, Tim R. Moore

Bibliographic record

VenueBotany · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSphagnumTransectPhosphorusBogNitrogenBotanyNutrientDeposition (geology)BiologyAnimal sciencePeatEcologyChemistry

Abstract

fetched live from OpenAlex

We examined concentrations of nitrogen (N) and phosphorus (P) and δ 15 N value in Sphagnum sections Acutifolia and Cuspidata inhabiting hummocks and hollows from eight bogs along a transect from ∼45 to ∼55°N in Ontario and Quebec. The N concentration in Sphagnum declined from south to north, correlating with a decrease in atmospheric N deposition. Although the overall N concentration was larger in hollows than hummocks, the pattern was inconsistent across the sites. There was a proportionally larger decline in P concentration from south to north and an overall larger P concentration in hollows than hummocks, but there were inconsistent differences across the sites. The N:P ratio ranged from 12:1 to 29:1, driven primarily by the variation in P concentration. Ratios of N and P concentration in Sphagnum capitulum:stem averaged 1.2:1, suggesting nutrient resorption from stem to capitulum during growth; the ratio rose with increasing N and P concentration in the capitulum. The δ 15 N value of Sphagnum rose from ∼−6‰ in the south to ∼−1‰ in the north, correlated with the decrease in Sphagnum N concentration and with a rise in the water table. We interpret this to indicate a greater dependence on N 2 -fixation for N acquisition in the northern and wetter sites.

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 score0.384
Threshold uncertainty score0.455

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 head0.006
GPT teacher head0.202
Teacher spread0.196 · 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

Citations17
Published2017
Admission routes4
Has abstractyes

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