Development and testing of a simple field-based intermittent-flow respirometry system for riverine fishes
Bibliographic record
Abstract
By understanding range-wide intraspecific variation in metabolic rate we can better understand how organisms have adapted to their environment. However, methods to quantify metabolic rate of fishes from remote areas or those that cannot be brought back to the laboratory because of imperilment status are lacking. Consequently, practical and reliable field-based methods are needed. To address this need, we developed a simple yet robust intermittent-flow respirometry system, adapted from a design commonly used in the laboratory that is readily suited for field use. Standard metabolic rate (SMR), maximum metabolic rate (MMR) and aerobic scope (AS) estimates were obtained from juvenile lake trout (Salvelinus namaycush) and brook trout (Salvelinus fontinalis) using both field- and laboratory-based systems. Whole-fish SMR, MMR and AS estimates from the field and laboratory methods did not differ from one another (ANCOVA and LMM: all P > 0.05) for either species and were comparable to estimates previously reported. Our field setup is a simpler system than the conventional laboratory-based system that requires less power and equipment to operate, yet still offers users the ability to: (1) acclimate fish to the respirometry chamber; (2) measure oxygen consumption during a shorter period (1 h), which yield metabolic rate estimates comparable to systems that take measurements over longer periods; and (3) take repeated oxygen consumption measurements with manual user-defined flush and measurement phase routines. Developing practical and reliable field respirometry methods, as demonstrated here, is important if we wish to improve our ability to predict how imperiled species will respond to changes in their environment. Such knowledge is critical for informing conservation strategies.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".