Studies of the effects of logging on stream cutbanks and of the occurrence of cutbanks as related to land characteristics
Bibliographic record
Abstract
A study was undertaken to determine the effect of streambank logging practices on salmonid cover in four streams in north central British Columbia. Undercut streambanks were chosen for measurement since they constitute a component of cover that is easily disturbed by logging activity. Sections of streams flowing through winter and summer logged regions were classified as either heavily or moderately disturbed and compared to adjacent unlogged sections with regard to stream widths and cutbank areas. Logging resulted in increased stream widths and decreased cutbank areas particularly in the heavily disturbed sections. Results indicate that skidder operators should avoid activity in and immediately adjacent to streams. For winter logging, this can be facilitated by marking streams prior to snowfall. A second study was undertaken in an attempt to develop a model for stream surveys which would permit prediction of cutbank formation by identifying associated land and stream characteristics on air photos. With the use of 1:63,000 air photographs, several streams in the Robertson River Watershed on Vancouver Island were divided into homogeneous units on the basis of the landform adjacent to the stream, valley shape, and stream pattern. Nine sections were compared with regard to cutbank area. No simple relationships between land and stream characteristics were found, making it impossible to develop a reliable model for predicting potential bank cover with the use of air photos alone. However, it was concluded that it is valid to use the survey techniques described to divide streams into relatively homogeneous units and determine locations warranting ground checks.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".