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Selective herbivory on a nitrogen fixing legume (<i>Lathyrus venosus</i>) influences productivity and ecosystem nitrogen pools in an oak savanna

2000· article· en· W1788438719 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEcoscience · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsEcosystemHerbivoreProductivityEcologyLathyrusNitrogen cycleAgronomyBiologyTerrestrial ecosystemEnvironmental scienceNitrogenChemistry

Abstract

fetched live from OpenAlex

Herbivory is known to change the structure of vegetation, but the possible effects of herbivory on ecosystem nitrogen pools are not well documented. Here we report that 13 years of deer exclusion significantly influenced ecosystem nitrogen pools and caused ecosystem productivity to more than double in a regularly burned Minnesota oak savanna. Herbivore exclusion greatly increased the abundance of Lathyrus venosus, a native nitrogen fixing legume. Primary productivity also increased through time, as did total soil nitrogen. This increase in productivity did not occur in unfenced plots, where there was a loss of total soil nitrogen, probably because fire-induced nitrogen losses exceeded gains. This study documents that herbivores, through “top-down” effects on foodwebs, can strongly influence nitrogen pools in terrestrial ecosystems, and that legumes can play a critical role in replacing fire-induced nitrogen losses in Midwestern oak savannas.

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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.007
GPT teacher head0.218
Teacher spread0.211 · 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