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Record W2185069524

The importance of seasonality in the timing of flora surveys in the South and Central Western Slopes of New South Wales.

2004· article· en· W2185069524 on OpenAlexfundno aff
Geoffrey E. Burrows

Bibliographic record

VenuePublication Server of Goethe University Frankfurt am Main (Goethe University Frankfurt) · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland and Wildlife Management
Canadian institutionsnot available
FundersBarrick Gold Corporation
KeywordsGeographyQuadratHerbaceous plantEcologyWoodlandVegetation (pathology)Species diversityPlant communityMediterranean climatePerennial plantSeasonalityUnderstoryForestryShrubBiologySpecies richnessCanopyArchaeology
DOInot available

Abstract

fetched live from OpenAlex

Semi-permanent quadrats, located in the South and Central Western Slopes botanical regions of New South Wales, were assessed to indicate suitable periods of the year to conduct surveys of botanical diversity. The quadrats were located in woodland communities with a generally herbaceous understorey, and subject to a wide range of domestic stock grazing intensities. In the mid to western South Western Slopes (SWS) the greatest number of species was generally recorded in an October survey. The main exception was in degraded areas (low species diversity, high proportion of annual weed species), where similar results were recorded in September and October. In the cooler and wetter eastern SWS a relatively high proportion of species were recorded in October to early December surveys. However, when compared to species totals compiled from multiple assessments in all seasons, or from August to November, a single optimal survey usually recorded only 60–75% of the plant species at a site. Surveys in mid to late summer, autumn and early winter usually recorded less than 50% of the plant species present. The results reflect the prevailing Mediterranean-type climate, and that the ground layer vegetation (primarily comprised of annuals and herbaceous perennials) dominates the species diversity.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.707

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.018
GPT teacher head0.202
Teacher spread0.184 · 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

Citations13
Published2004
Admission routes1
Has abstractyes

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