Reply to comment by Jackson and Martin on “Does timber harvest influence the dynamics of marine-derived nutrients in Southeast Alaska streams?” <sup>1</sup>Original article by Levi et al. appears in Can. J. Fish. Aquat. Sci. <b>68</b>(8): 1316–1329 and is available at http://www.nrcresearchpress.com/doi/full/10.1139/f2011-067. Comment by Jackson and Martin appears in Can. J. Fish. Aquat. Sci. <b>69</b>: this issue, and is available at http://www.nrcresearchpress.com/doi/full/10.1139/f2012-104.
Bibliographic record
Abstract
The stated goal of Levi et al. (2011, Can. J. Fish. Aquat. Sci. 68: 1316–1329) was to determine the influence of geomorphic complexity on the dynamics of salmon-derived nutrients. We studied seven streams in Southeast Alaska with varying degrees of historical (mid-1900s) timber harvest and, as a result, differences in stream geomorphology. In a comment on our study, Jackson and Martin (2012, Can. J. Fish. Aquat. Sci. 69: this issue) suggest that the geomorphic complexity we ascribe to timber harvest may be due to natural variation in watershed characteristics and offer alternative hypotheses. We sought to reduce the natural variation among our study streams by using a stratified sampling design (i.e., selecting reaches classified as floodplain 4 or 5 by the USDA Forest Service), but acknowledge that, as with any ecological field study, alternative hypotheses may exist to explain observed patterns in ecological responses. We maintain that our study design was sufficiently robust (i.e., 300 m reaches studied in seven streams for 3 years, totaling 21 stream-years) to draw inferences about the influence of salmon on streamwater nutrients and, secondarily, the role of geomorphic variation in mediating nutrient dynamics. Our data also support our finding that the legacy of timber harvest altered nutrient dynamics in salmon-bearing streams via alterations to stream geomorphology that were quantifiable.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.037 | 0.039 |
| Insufficient payload (model declined to judge) | 0.010 | 0.010 |
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".