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Record W1600525933 · doi:10.22621/cfn.v121i2.444

Notes on Succession in Old Fields in Southeastern Ontario: the Herbs

2007· article· en· W1600525933 on OpenAlexafffundvenueabout
Adèle A. Crowder, R. Harmsen, Suzanne Blatt

Bibliographic record

VenueThe Canadian Field-Naturalist · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland and Wildlife Management
Canadian institutionsQueen's University
FundersQueen's UniversityMinistry of Natural Resources
KeywordsSpecies richnessBiologyEcological successionForbVegetation (pathology)Plant communityShrubGrasslandWeedWoody plantPerennial plantEcologyBotanyAgronomy

Abstract

fetched live from OpenAlex

Vegetation in abandoned hayfields was monitored during 1976-1998. An earlier successional stage followed ploughing. Changes in tree, shrub and vine populations have been reported earlier and showed expected increases in species richness and cover. Highest species richness of herbs occurred three years after ploughing. Non-woody species richness trended irregularly downward, while non-woody cover was variable, peaking in 1987. Within the herbaceous community, year-to-year changes in cover and frequency of species in the following selected groups are reported here: 18 grasses including sown and adventive species; 13 legumes including two sown species; 14 macroforbs of the Compositae, including a goldenrod, Solidago canadensis, which dominated parts of the fields; a rosette weed, Taraxacum officinalis; sedges, horsetails and some other minor components. Grasses and goldenrods were grazed, sometimes intensively and repeatedly, by insects; grasses were impacted by skipper larvae (Thymelicus lineola), and goldenrods by beetle larvae (Trirhabda spp.). Effects of repeated outbreaks on host plant cover are shown for two plots (100 m2) matching the scale of outbreaks.

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 score0.160
Threshold uncertainty score0.323

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.012
GPT teacher head0.222
Teacher spread0.210 · 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

Citations1
Published2007
Admission routes4
Has abstractyes

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