Bibliographic record
Abstract
Perched on a rocky ledge 400 m above flatter ground, fingers delicately searching for the right hold, one tends to appreciate the nature of balance. Elkhorn is Vancouver Island’s second highest peak at 2195 m, and our traverse was taking us across a bluffy section near the top. A miscalculation at the smallest scale would have severe, and for whoever survived, perhaps unknown consequences. Of course the unforeseen often does occur, and the more complex and dynamic the system, the harder it is to predict the long term outcome. The precarious and occasionally unexpected interactions of the geomorphogical world with the biological have been highlighted in several issues of Island Geoscience, and that theme is continued here. David Stauth of Oregon State University reports on some of the recent work by Robert Beschta that links land management decisions impacting wolves to channel stability. At the summit, the roof of Vancouver Island, one was able look at “big picture geomorphology”, see major processes at work and speculate on balance at a different scale. It is with that big picture view that we are using new technologies such as satellite imagery, climate models and change detection to consider other, coarser questions of balance, and how we are impacting and being impacted by the processes around us. I hope you enjoy the articles. Island Geoscience welcomes new submissions or ideas for articles. The newsletter is sent to a few hundred professionals in government and private industry in BC and abroad. Please send ideas or
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.019 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.053 | 0.020 |
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".