Zooplankton production in Lake Ontario: a multistrata approach
Bibliographic record
Abstract
We present evidence that estimates of annual zooplankton production in Lake Ontario based on stratified plankton net tows are preferable to those based on single, integrated tows through the whole water column. Samples were collected roughly every 2 weeks during 1993 and 1994 at a deep midlake station and at a shallower nearshore station. Stratified net tows were taken through each of the epilimnion, metalimnion, and hypolimnion and the mean temperatures of these strata used in production calculations. Integrated net tows were taken through the whole water column and the mean temperature of the column used in production calculations. Production estimates based on stratified samples were consistently higher (up to 310%) than those based on integrated samples. We show that higher stratified estimates are partly due to higher plankton net filtering efficiency over the shorter stratified hauls and partly due to the lower mean temperatures used in integrated hauls. Our analyses provide a basis for approximating the error in integrated production estimates caused by temperature stratification, water column depth, and plankton net efficiency.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".