Testing the roles of local adaptation and genetic diversity to improve Giant kelp (Macrocystis pyrifera) restoration
Bibliographic record
Abstract
This dataset includes survey and environmental data from a manipulative field experiment testing the roles of local adaptation and genetic diversity in giant kelp (Macrocystis pyrifera) restoration success. Kelp spores were sourced from parent kelp individuals at two sites in Barkley Sound, British Columbia, Canada: Ed King Island (cooler on average) and Dixon Island (warmer on average). Kelp were then outplanted back at the two sites in a reciprocal transplant design. The goal of this experiment was to test whether giant kelp grows better at its home site than at an away site, and whether kelp cultures with a greater number of parents (higher genetic diversity) perform better. The dataset consists of survival (number gravels with live kelp out of gravels remaining in plot) and growth (total length of the longest kelp on five randomly selected gravels per plot) estimates at four timepoints (April, May, June, August) taken by SCUBA divers. Other environmental data include grazer counts in each plot, substrate composition of each plot, grazer transect surveys at each site, nutrient measurements 1m from surface and 1m from bottom, and temperature and salinity profiles. Some measurements from kelp in the nursery prior to outplanting are also provided.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.013 |
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".