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Record W7132938234

The influence of non-nitrogenous nutrient additions on growth and physiology of sugar maple and composition of understory vegetation in northern hardwoods of central Ontario

2006· dissertation· W7132938234 on OpenAlexaboutno aff
Tomasz Aleksander Gradowski

Bibliographic record

VenueTSpace · 2006
Typedissertation
Language
FieldEnvironmental Science
TopicSeedling growth and survival studies
Canadian institutionsnot available
Fundersnot available
KeywordsUnderstoryCanopyNutrientForest floorTree canopyTemperate rainforestTemperate climateForest ecologyVegetation (pathology)
DOInot available

Abstract

fetched live from OpenAlex

While it is widely assumed that most temperate forests are nitrogen limited, many stands located in areas affected by high N deposition exhibit signs of nitrogen excess. Symptoms include an increase in nitrate concentration and a decrease in the C:N ratio of both soil and foliage; thus affected ecosystems are expected to experience a significant reduction in their natural nitrogen limitation. This process is likely accompanied by changes in availability of phosphorus and basic cations. This work demonstrates that N-induced deficiency of P, K, Ca and Mg is evident in sugar maple dominated northern hardwoods of central Ontario. Research presented here also examines several interrelated aspects of forest responses to additions of non-N mineral nutrients, including their effects on forest floor and mineral soil, diameter increment of trees, canopy physiology and morphology, and diversity of understory vegetation. This thesis demonstrates that the addition of truly growth-limiting nutrients does increase tree growth even among slow-growing, tolerant species such as sugar maple. Furthermore, growth increase is evident among mature and young trees within a relatively short time if monitoring of tree canopy response is included in research. This work also shows that while liming and non-N nutrient additions help alleviate many negative effects of nitrate pollution, they also escalate the process of forest eutrophication, which influences diversity of understory vegetation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.267
Threshold uncertainty score0.538

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.228
Teacher spread0.221 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2006
Admission routes1
Has abstractyes

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