Spatial variability of stream bed scour and fill: a comparison of scour depth in chum salmon (<i>Oncorhynchus keta</i>) redds and adjacent bed
Bibliographic record
Abstract
Scour depth in egg pockets of chum salmon (Oncorhynchus keta) egg nests (redds) in a short gravel-bed spawning reach (45 × 20 m) of Kanaka Creek, British Columbia, was not significantly different from that in the adjacent bed during 1997-1998 winter flood events, whereas the scour depth in tailspills of redds was greater. Over the course of the incubation period, none of the egg pocket locations (zero of four), all of the tailspills (three of three), and 17% of the immediately adjacent bed locations (three of 18) scoured to the assumed egg burial depth of 15 cm below the initial postspawning surface elevation. Egg pocket scour depth has not previously been monitored, and the reliance of earlier studies on tailspill monitoring as an indication of redd scour may have led to faulty assessment of embryo loss. Only one flood event, which exceeded bankfull, caused widespread and deep scour and fill. Despite implementation of the most spatially intensive array of wiffle-ball scour monitors to date, scour was so variable that there was no spatial autocorrelation of scour depths.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".