Reporting guidelines for terrestrial respirometry: Building openness, transparency of metabolic and evaporative water loss data
Bibliographic record
Abstract
Respirometry is an important procedure for understanding whole-animal energy and water balance. Consequently, the growing number of studies using respirometry over the last decade warrants reliable reporting and data sharing for effective research synthesis and dissemination. We provide a checklist guideline on five key areas to facilitate the transparency and reproducibly of respirometry studies: 1) materials, set up, plumbing, 2) subject conditions/maintenance, 3) measurement conditions, 4) data processing, and 5) data reporting and statistics, each with explanations and example studies. Transparency in reporting and data availability has benefits on multiple fronts. Authors can use this checklist in the designing and reporting their study, and reviewers and editors can use the checklist to assess reporting quality in the manuscripts they review. Improved standards for reporting will enhance the value of primary studies and will greatly facilitate the ability to carry out higher quality research syntheses to address ecological and evolutionary theories, and provide reliable evidence-based information for political actions in the face of global change.
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 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 | 0.408 | 0.727 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.030 | 0.028 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".