MétaCan
Menu
Back to cohort
Record W7099111581

@ 1979, by the American Society of Limnology and Oceanography, Inc. Zooplankton grazing and phytoplankton species richness: Field tests of the predation hypothesis1

2015· article· en· W7099111581 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicAsian Studies and History
Canadian institutionsnot available
Fundersnot available
KeywordsPredationGrazingCompetition (biology)HerbivoreIntertidal zoneZooplanktonPhytoplanktonAlgae
DOInot available

Abstract

fetched live from OpenAlex

The hypothesis that herbivory plays a major role in the maintenance of high algal diversity was tested over a e-year period in polyethylene enclosures at Heney Lake, Quebec. Reduction of grazing pressure resulted in a significant decrease in the number of inedible species, whereas the diversity of edible algae remained unaffected. Lowering the herbivore levels may cause an intensification of exploitative competition among phytoplankton, which favors edible species and a few inedible algae like Synedra at the expense of many inedible species that are eliminated. Predation has been implicated as a po-tential factor allowing the coexistence of competing species ever since Darwin (1859) recorded the effect of mowing on grassland plant communities. Indeed grazing was one of the factors initially proposed by Hutchinson (1961) to ex-plain the surprisingly high diversity ob-served in lake phytoplankton. Yet, while the high degree of temporal and spatial overlap found among phytoplankton species in nature (Wall and Briand in prep.) suggests low competition levels that are quite compatible with this hy-pothesis, very few workers (Porter 1973, 1977; O’Brien 1974) have investigated this possibility. The predation hypothesis was first clearly formulated as such by Paine (1966) to account for the diversity pat-terns of intertidal communities on rocky shores. He demonstrated that predation can promote prey diversity by allowing the local coexistence of prey species that would otherwise be eliminated by com-petitive exclusion. A large body of theo-retical evidence has since accumulated to isolate the parameters affecting the op-eration of such a mechanism (e.g. Spight

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 score0.322
Threshold uncertainty score0.578

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 studies0.0000.002
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 head0.029
GPT teacher head0.265
Teacher spread0.236 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

Explore more

Same topicAsian Studies and HistoryFrench-language works237,207