Evaluation of a Simple Method to Classify the Thermal Characteristics of Streams Using a Nomogram of Daily Maximum Air and Water Temperatures
Bibliographic record
Abstract
Abstract This study assessed the applicability of an existing methodology to classify different stream sites into coldwater, coolwater, or warmwater areas based on their maximum air and water temperatures in summer. Using this methodology, single measurements of daily maximum air temperatures (≥24.5°C) and water temperatures at 1600 hours between July 1 and September 7 can be plotted on a nomogram to approximate the thermal classification of a site. Data from 122 sites throughout the Great Lakes basin, Ontario, indicated that the existing methodology should be revised to include sampling days from July 1 to August 31 instead of July 1 to September 7 and daily sampling periods between 1600 and 1800 hours as opposed to 1600 hours to capture the warmest temperatures at the sites. Data from 80 of the 122 sites consistently fell into the coldwater, coolwater, or warmwater categories. Data from 5 sites overlapped the coldwater and coolwater categories, whereas data from 37 sites overlapped the coolwater and warmwater categories. Water temperatures at 11 of the coolwater–warmwater sites decreased as air temperatures increased. These sites had more groundwater discharge potential than either the coolwater and warmwater sites. This suggests that a higher proportion of groundwater (as opposed to surface runoff) in the summer caused the water temperatures to cool as air temperatures increased. A revised nomogram was developed that included five (cold, cold–cool, cool, cool–warm, and warm) rather than three (cold, cool, and warm) thermal classifications for sites in Ontario streams. We recommend the use of the revised nomogram to determine the thermal classification of stream sites, as the revised categories also correspond to the thermal preferences of 72 stream fish species commonly found in the Great Lakes basin.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".