Code and data: Temporal variability declines with increasing trophic levels and spatial scales in freshwater ecosystems
Bibliographic record
Abstract
Code and data to reproduce the results in Siqueira et al. (submitted) published as a Preprint (https://ecoevorxiv.org/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: 00_Renv_help_reproduc.R 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. This is a collaborative effort and not all authors are allowed to share their raw data. For example, one data set (LEPAS) was not made available due to data sharing policies of The Ohio Division of Wildlife (ODOW). The orginal raw data include the abundance (individual counts, biomass, coverage area) of a given taxon, at a given site, in a given year. See details here https://ecoevorxiv.org/mpf5x Thus, here, instead of starting the analyses with raw data, we start with data that has been generated with the code: 01_Dataprep_stability_metrics.R These data include 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. Reproducibility To improve reproducibility, I’m using the R package renv to register the versions of the R packages I use and to manage a local library that doesn’t affect the rest of my system. renv::init() is used to prepare a lockfile that records the exact versions of R packages I used in this project. 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 00 to 05. I am making the whole R project folder (with everything needed to reproduce the results) available as a compressed file: Stab_R_project_full.zip
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.306 | 0.146 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".