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Record W270263830

Chemical property of live and dead leaves of tundra plant species in Oobloyah Valley, Ellesmere Island, high arctic Canada

2006· article· en· W270263830 on OpenAlexaboutno aff
Takashi Osono, Akira Mori, Masaki Uchida, Hiroshi Kanda

Bibliographic record

VenueInstitutional Repositories DataBase (IRDB) · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
Fundersnot available
KeywordsTundraArcticArctic vegetationEcologyGeographyThe arcticEnvironmental scienceOceanographyBiologyGeology
DOInot available

Abstract

fetched live from OpenAlex

The chemical property of live and dead leaves was investigated regarding 14 plant species, including shrubs, forbs, graminoids, and mosses. Leaves were collected from a deglaciated terrain in Ellesmere Island, high arctic Canada. The contents of organic chemical components (lignin, total carbohydrates, extractives), carbon, and nutrients (N, P, K, Ca, Mg) were examined and compared among the species. In general, forbs had high content of nutrients and low content of carbon and organic chemical components; shrubs had high content of carbon and lignin and low content of nutrients; graminoids had high content of total carbohydrates and low content of lignin and nutrients; and mosses had high content of total carbohydrates and markedly low content of nutrients. Principal component analysis showed separation of clusters of shrubs, forbs, graminoids, and mosses. The trend was generally similar between live and dead leaves.

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 score0.370
Threshold uncertainty score0.745

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
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 head0.017
GPT teacher head0.192
Teacher spread0.175 · 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

Citations8
Published2006
Admission routes1
Has abstractyes

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