MétaCan
Menu
Back to cohort
Record W2064717713 · doi:10.1139/z04-126

Influence of fire, topography, and consumer abundance on seed predation in tallgrass prairie

2004· article· en· W2064717713 on OpenAlexvenueno aff
Aaron W. Reed, Glennis A. Kaufman, Donald W. Kaufman

Bibliographic record

VenueCanadian Journal of Zoology · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyPredationInvertebrateNocturnalAbundance (ecology)EcologyHabitatLitterSeed predationSeed dispersalBiological dispersalPopulation

Abstract

fetched live from OpenAlex

We assessed seed predation by vertebrates and invertebrates in three fire-frequency treatments (<1 year, 1–4 years, and >4 years since fire) and in three topographic positions (upland, limestone breaks, and lowland) in tallgrass prairie. Two types of seed trays, one for vertebrates and one for invertebrates, were placed in each treatment during each nocturnal and diurnal period. Vertebrates removed significantly more seeds than did invertebrates. Fire frequency and topographic position affected seed removal by both vertebrates and invertebrates. Seed removal by invertebrates was influenced negatively by fire; the greatest seed removal occurred in uplands and lowlands in unburned prairie. Vertebrates removed the most seeds in burned prairie and in lowlands and limestone breaks. Time of day also influenced seed removal by vertebrates, as nocturnal vertebrates (assumed to be rodents) removed more seeds than diurnal vertebrates. Abundance of rodents, however, did not predict accurately seed removal in fire treatments or topographic positions, as rodents removed fewer seeds than expected in prairie that had not been burned in >4 years and in lowlands. This pattern likely was due to the presence of a well-developed plant litter layer in both unburned and lowland habitats, which reduces the likelihood of a rodent locating seeds.

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)0.000
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 score0.240
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.008
GPT teacher head0.220
Teacher spread0.212 · 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

Citations35
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of ZoologySame topicAnimal Ecology and Behavior StudiesFrench-language works237,207