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Record W4251297528 · doi:10.26686/wgtn.12469859

Decline in New Zealand's freshwater fish fauna: Effect of land use

2020· preprint· en· W4251297528 on OpenAlexaff
Michael Joy, KJ Foote, P McNie, Marina Piria

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversité du Québec à Montréal
FundersSveučilište u Zagrebu
KeywordsIndex of biological integrityThreatened speciesFaunaFreshwater fishGeographyLand coverAbundance (ecology)Drainage basinVegetation (pathology)EcologyPastureFish <Actinopterygii>Land useFisheryEnvironmental scienceBiologyForestryHabitat

Abstract

fetched live from OpenAlex

© 2019 CSIRO. The number of New Zealand's freshwater fish listed as threatened has increased since 1992 when the first New Zealand threat classification system list was compiled. In this study, temporal and land cover-related trends were analysed for data on freshwater fish distribution, comprising more than 20 000 records for the 47 years from January 1970 to January 2017 from the New Zealand Freshwater Fish Database. The analysis included individual species abundance and distribution trends, as well as an index of fish community integrity, namely the Index of Biotic Integrity (IBI). Of the 25 fish species that met the requirements for analysis to determine changes in the proportion of sites they occupied over time, 76% had negative trends (indicating declining occurrence). Of the 20 native species analysed for the proportion of sites occupied over time, 75% had negative trends; 65% of these were significant declines and more species were in decline at pasture sites than natural cover sites. The average IBI score also declined over the time period and, when analysed separately, the major land cover types revealed that the IBI declined at pasture catchment sites but not at sites with natural vegetation catchments.

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)0.002
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 score0.408
Threshold uncertainty score0.811

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.014
GPT teacher head0.235
Teacher spread0.221 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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