MétaCan
Menu
Back to cohort
Record W2111219132 · doi:10.1093/forestry/cpp027

Snag frequency, diameter and species distribution and input rate in Newfoundland boreal forests

2010· article· en· W2111219132 on OpenAlexaffabout
M. T. Moroni, Denis. Harris

Bibliographic record

VenueForestry An International Journal of Forest Research · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsNatural Resources CanadaCanadian Forest Service
FundersU.S. Forest Service
KeywordsSnagBalsamAbies balsameaTaigaForestryBlack spruceDiameter at breast heightThinningDead treeHectareEcologyEnvironmental scienceBiologyBotanyGeographyHabitat

Abstract

fetched live from OpenAlex

Dead-tree (snag) density (stems per hectare), diameter, species distribution and input rate were examined in Newfoundland boreal forests dominated by black spruce (Picea mariana (Mill.) B.S.P.) and balsam fir (Abies balsamea (L.) Mill.). Examinations were based on permanent sample plot data. Total snag densities were higher in fir than spruce and were densest in 40- to 59-year-old stands of both species. Densities of >9 cm diameter at breast height (d.b.h.; 1.3 m, hereafter all dimensions are dbh) snags and live trees and larger live and dead trees (>19 cm stems) were also higher in fir than spruce. Fir also generated more larger snags at a younger age than spruce. Precommercial thinning reduced snag densities, virtually eliminating >9 cm snags from 37- to 48-year-old forests. Disturbance regime had a minor impact on snag densities in >40-year-old forests. The annual rate of >9 cm snag production in >60-year-old forests was <1.6 and <1.2 per cent of live trees per year in fir and spruce, respectively. The annual rate of live >9 cm tree fall down in >60-year-old forests was <0.6 and <0.9 per cent in fir- and spruce-dominated forests, respectively.

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)0.001
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 score0.037
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.041
GPT teacher head0.306
Teacher spread0.264 · 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

Citations7
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueForestry An International Journal of Forest ResearchSame topicForest Ecology and Biodiversity StudiesFrench-language works237,207