Recovery of fish populations in Lake 223 from experimental acidification
Bibliographic record
Abstract
The fish populations of Lake 223, a lake previously acidified to pH 5.1, were monitored during 13 years of gradual pH recovery to preacidification pH 6.7. During acidification, recruitment ceased for all fish species in this lake and two were extirpated. During pH recovery, successful recruitment resumed for all fish species that remained in the lake. One of the extirpated species, fathead minnow (Pimephales promelas), successfully colonized the lake. Lake trout (Salvelinus namaycush) abundance decreased during acidification and remained low during pH recovery due to very low recruitment. Growth curves, condition factor, and annual survival of lake trout decreased during acidification and quickly increased to preacidification values during pH recovery. During the early years of pH recovery, white sucker (Catostomus commersoni) abundance increased to almost 10 times the number at the start of the experiment but decreased during the final years due to decreased annual survival and recruitment. Pearl dace (Margariscus margarita) became abundant during acidification and their abundance decreased during pH recovery as fathead minnow abundance increased. Other fish species that were caught infrequently prior to acidification, brook stickleback (Culaea inconstans), lake chub (Couesius plumbeus), and finescale dace (Phoxinus neogaeus), were caught frequently during pH recovery.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".