MétaCan
Menu
Back to cohort
Record W2061360473 · doi:10.1139/x05-156

Comparison of three sampling methods in the characterizationof cork oak stands for management purposes

2005· article· en· W2061360473 on OpenAlexvenueno aff
Maria‐João Paulo, Margarida Tomé, A. Otten, Alfred Stein

Bibliographic record

VenueCanadian Journal of Forest Research · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicRemote Sensing and LiDAR Applications
Canadian institutionsnot available
FundersFundação para a Ciência e a Tecnologia
KeywordsCorkQuercus suberSampling (signal processing)StatisticsEvergreenMathematicsCluster samplingFagaceaeEstimatorForestrySystematic samplingEnvironmental scienceEcologyBotanyComputer scienceGeographyBiologyPopulation

Abstract

fetched live from OpenAlex

The cork oak (Quercus suber L.) is an evergreen oak that has the ability to produce a continuous layer of cork tissue which regenerates after being removed. Cork oak stands can be diverse in structure. Young stands are often regularly spaced, whereas older stands usually show clustering and can be mixed with other species. Farmers assessing cork value use a zigzag sampling procedure within a stand. In this study we compare zigzag sampling with two other sampling methods, fixed-radius plot sampling and n-tree distance sampling, using a model for the costs of sampling. We used data from two cork oak stands in Portugal as well as data from six types of simulated stands. We found that zigzag is the poorest sampling method, as in most situations it produces estimators with larger bias and larger standard errors than that produced by the other two procedures. Fixed-radius plot sampling and n-tree distance sampling produce comparable results; however, fixed-radius plot sampling is preferred because it produces unbiased estimators.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.541
Threshold uncertainty score0.932

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.170
GPT teacher head0.451
Teacher spread0.281 · 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

Citations11
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Forest ResearchSame topicRemote Sensing and LiDAR ApplicationsFrench-language works237,207