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Record W6948301461 · doi:10.5061/dryad.p0bh4

Data from: The jellification of north temperate lakes

2014· dataset· en· W6948301461 on OpenAlexaffabout

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2014
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Phylogenetic Studies
Canadian institutionsUniversity of OttawaLaurentian UniversityYork UniversityToronto Metropolitan UniversityQueen's University
Fundersnot available
KeywordsZooplanktonDominance (genetics)Temperate climatePelagic zoneCladoceraDaphniaHerbivore

Abstract

fetched live from OpenAlex

Calcium (Ca) concentrations are decreasing in softwater lakes across eastern North America and western Europe. Using long-term contemporary and palaeo-environmental field data, we show that this is precipitating a dramatic change in Canadian lakes: the replacement of previously dominant pelagic herbivores (Ca-rich Daphnia species) by Holopedium glacialis, a jelly-clad, Ca-poor competitor. In some lakes, this transformation is being facilitated by increases in macro-invertebrate predation, both from native (Chaoborus spp.) and introduced (Bythotrephes longimanus) zooplanktivores, to which Holopedium, with its jelly coat, is relatively invulnerable. Greater representation by Holopedium within cladoceran zooplankton communities will reduce nutrient transfer through food webs, given their lower phosphorus content relative to daphniids, and greater absolute abundances may pose long-term problems to water users. The dominance of jelly-clad zooplankton will likely persist while lakewater Ca levels remain low.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.093
Threshold uncertainty score0.185

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0390.020

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 head0.028
GPT teacher head0.254
Teacher spread0.227 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2014
Admission routes2
Has abstractyes

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