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Record W4394059175 · doi:10.5281/zenodo.8333128

Code and data: Understanding temporal variability across trophic levels and spatial scales in freshwater ecosystems

2023· dataset· en· W4394059175 on OpenAlexaff
Tadeu Siqueira, Charles P. Hawkins, Julian D. Olden, Jonathan D. Tonkin, Lise Comte, Victor S. Saito, Thomas L. Anderson, Gedimar Pereira Barbosa, Núria Bonada‬‬‬‬‬‬‬‬‬‬‬, Cláudia Cósta Bonecker, Miguel Cañedo‐Argüelles, Thibault Datry, Michael B. Flinn, Pau Fortuño, Gretchen A. Gerrish, Peter Haase, Matthew J. Hill, James M. Hood, Kaisa‐Leena Huttunen, Michael Jeffries, Timo Muotka, Daniel R. O’Donnell, Riku Paavola, Petr Pařil, Michael J. Paterson, Christopher J. Patrick, Gilmar Perbiche‐Neves, Luzia Cleide Rodrigues, Susanne C. Schneider, Michal Straka, Albert Ruhí

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2023
Typedataset
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsInternational Institute for Sustainable Development
Fundersnot available
KeywordsTrophic levelCode (set theory)Temporal scalesEnvironmental scienceEcosystemGeographyEcologyComputer scienceBiology

Abstract

fetched live from OpenAlex

Code and data to reproduce the results in Siqueira et al. (submitted) published as a Preprint (https://doi.org/10.32942/osf.io/mpf5x) The full set of results, including those made available as supplementary material, can be reproduced by running five scripts in the R_codes folder following this sequence: 01_Dataprep_stability_metrics.R 02_SEM_analyses.R 03_Stab_figs.R 04_Stab_supp_m.R 05_Sensit_analysis.R and using the data available in the Input_data folder. The original raw data made available include the abundance (individual counts, biomass, coverage area) of a given taxon, at a given site, in a given year. See details here https://doi.org/10.32942/osf.io/mpf5x However, this is a collaborative effort and not all authors are allowed to share their raw data. One data set (LEPAS), out of 30, was not made available due to data sharing policies of The Ohio Division of Wildlife (ODOW). So, in code "01_Dataprep_stability_metrics.R" all data made available are imported, except the LEPAS data set. For this specific data set, code "01_Dataprep_stability_metrics.R" imports variability and synchrony components estimated using the methods described in Wang et al. (2019 Ecography; doi/10.1111/ecog.04290), diversity metrics (alpha and gamma diversity), and some variables describing the data set. A protocol for requesting access to the LEPAS data sets can be found here: https://ael.osu.edu/researchprojects/lake-erie-plankton-abundance-study-lepas Dataset owner: Ohio Department of Natural Resources – Division of Wildlife, managed by Jim Hood, Dept. of Evolution, Ecology, and Organismal Biology, The Ohio State University. Email: hood.211@osu.edu Anyone who wants to reproduce the results described in the preprint can just download the whole R project (that includes code and data) and run codes from 01 to 05. I am making the whole R project folder (with everything needed to reproduce the results) available as a compressed file.

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)0.006
metaresearch head score (Gemma)0.048
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 score0.085
Threshold uncertainty score0.284

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.048
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0030.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0850.038

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 head0.090
GPT teacher head0.288
Teacher spread0.198 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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